bibtex.bib

@article{prabhu12356gdumb,
  title={GDumb: A Simple Approach that Questions Our Progress in Continual Learning},
  author={Prabhu, Ameya and Torr, Philip HS and Dokania, Puneet K},
  url={http://www.robots.ox.ac.uk/~tvg/publications/2020/gdumb.pdf},
  keywords={Survey},
  year={2020}
}
@String(prabhu12356gdumb="introduces a super simple methods that outperforms almost all methods in all of the CL benchmarks. We need new better benchamrks")


@inproceedings{ostapenko2019learning,
  title={Learning to remember: A synaptic plasticity driven framework for continual learning},
  author={Ostapenko, Oleksiy and Puscas, Mihai and Klein, Tassilo and Jahnichen, Patrick and Nabi, Moin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={11321--11329},
  year={2019},
  url={https://openaccess.thecvf.com/content_CVPR_2019/html/Ostapenko_Learning_to_Remember_A_Synaptic_Plasticity_Driven_Framework_for_Continual_CVPR_2019_paper.html},
  keywords={Generative Replay}
}
@String(ostapenko2019learning="introdudes Dynamic generative memory (DGM) which relies on conditional generative adversarial networks with learnable connection plasticity realized with neural masking")

@inproceedings{Gupta2020LaMAMLLM,
  title={La-MAML: Look-ahead Meta Learning for Continual Learning},
  author={Gunshi Gupta and Karmesh Yadav and Liam Paull},
  url={https://arxiv.org/abs/2007.13904},
  year={2020},
  keywords={"Meta-Continual Learning"}
}
@String(Gupta2020LaMAMLLM="Proposes an online replay-based meta-continual learning algorithm with learning-rate modulation to mitigate catastrophic forgetting")

@article{Ren2020WanderingWA,
  title={Wandering Within a World: Online Contextualized Few-Shot Learning},
  author={Mengye Ren and Michael L. Iuzzolino and Michael C. Mozer and Richard S. Zemel},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.04546},
  url={https://arxiv.org/abs/2007.04546},
  keywords={Continual Few-Shot Learning, Setting}
}
@String(Ren2020WanderingWA="proposes a new continual few-shot setting where spacial and temporal context can be leveraged to and unseen classes need to be predicted")


@misc{lesort2020continual,
    title={Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes},
    author={Timothée Lesort},
    year={2020},
    eprint={2007.00487},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2007.00487},
    keywords={Thesis},
}

@article{clicml2020,
  title={Workshop on Continual Learning at ICML 2020},
  author={Rahaf Aljundi, Haytham Fayek, Eugene Belilovsky, David Lopez-Paz, Arslan Chaudhry, Marc Pickett, Puneet Dokania, Jonathan Schwarz, Sayna Ebrahimi},
  journal={ICML},
  year={2020},
  url={https://sites.google.com/view/cl-icml/organizers?authuser=0},
  keywords={Workshops},
}


@article{lifelongml2020,
  title={4th Lifelong Machine Learning Workshop at ICML 2020},
  author={Shagun Sodhani and Sarath Chandar and Balaraman Ravindran and Doina Precup},
  journal={ICML},
  year={2020},
  url={https://openreview.net/group?id=ICML.cc/2020/Workshop/LifelongML#accept},
  keywords={Workshops},
}

@article{antoniou2020defining,
  title={Defining Benchmarks for Continual Few-Shot Learning},
  author={Antoniou, Antreas and Patacchiola, Massimiliano and Ochal, Mateusz and Storkey, Amos},
  journal={arXiv preprint arXiv:2004.11967},
  year={2020},
  url={https://arxiv.org/abs/2004.11967},
  keywords={Continual Few-Shot Learning, Setting},
}
@String(antoniou2020defining="(title is a good enough summary)")

@article{ahmad2017unsupervised,
  title={Unsupervised real-time anomaly detection for streaming data},
  author={Ahmad, Subutai and Lavin, Alexander and Purdy, Scott and Agha, Zuha},
  journal={Neurocomputing},
  volume={262},
  pages={134--147},
  year={2017},
  publisher={Elsevier},
  url={https://www.sciencedirect.com/science/article/pii/S0925231217309864},
  keywords={Applications}
}
@String(ahmad2017unsupervised="HTM applied to real-world anomaly detection problem")

@article{cui2016continuous,
  title={Continuous online sequence learning with an unsupervised neural network model},
  author={Cui, Yuwei and Ahmad, Subutai and Hawkins, Jeff},
  journal={Neural computation},
  volume={28},
  number={11},
  pages={2474--2504},
  year={2016},
  publisher={MIT Press},
  url={https://arxiv.org/abs/1512.05463},
  keywords={Applications}
}
@String(cui2016continuous="HTM applied to a prediction problem of taxi passenger demand")

@article{kiyasseh2020clops,
  title={CLOPS: Continual Learning of Physiological Signals},
  author={Kiyasseh, Dani and Zhu, Tingting and Clifton, David A},
  journal={arXiv preprint arXiv:2004.09578},
  year={2020},
  url={https://arxiv.org/abs/2004.09578},
  keywords={Applications}
}
@String(kiyasseh2020clops="a healthcare-specific replay-based method to mitigate destructive interference during continual learning")


@article{vanbrain,
  title={Brain-Like Replay For Continual Learning With Artificial Neural Networks},
  author={van de Ven, Gido M and Siegelmann, Hava T and Tolias, Andreas S},
  year={2020},
  url={https://baicsworkshop.github.io/pdf/BAICS_8.pdf},
  keywords={Generative Replay},
}

@incollection{Thrun95,
  title={Lifelong robot learning},
  author={Thrun, Sebastian and Mitchell, Tom M},
  booktitle={The biology and technology of intelligent autonomous agents},
  pages={165--196},
  year={1995},
  publisher={Springer},
  keywords={Classics},
  url={http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.71.3723&rep=rep1&type=pdf}
}
@String(Thrun95="Argues knowledge transfer is essential if robots are to learn control with moderate learning times")

@article{Farquhar18,
  title={Towards Robust Evaluations of Continual Learning},
  author={Farquhar, Sebastian and Gal, Yarin},
  journal={arXiv preprint arXiv:1805.09733},
  year={2018},
  keywords={Influentials, },
  url={https://arxiv.org/abs/1805.09733},
}
@String(Farquhar18="Proposes desideratas and reexamines the evaluation protocol")

@inproceedings{Chaudhry19,
  title={Efficient Lifelong Learning with A-GEM},
  author={Chaudhry, Arslan and Ranzato, Marc’Aurelio and Rohrbach, Marcus and Elhoseiny, Mohamed},
  booktitle={ICLR},
  year={2019},
  url={https://arxiv.org/abs/1812.00420},
    keywords={Rehearsal, Influential}
}
@String(Chaudhry19="More efficient GEM; Introduces online continual learning")

@article{kirkpatrick2017overcoming,
  title={Overcoming catastrophic forgetting in neural networks},
  author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},
  journal={Proc. of the national academy of sciences},
  year={2017},
  publisher={National Acad Sciences},
    keywords={Regularization, Influential},
    url={https://www.pnas.org/content/pnas/114/13/3521.full.pdf},
}
@String(Kirkpatrick17="Introduces prior-focused methods (Elastic Weight Consolidation)")


@incollection{Lopez-Paz17,
title={Gradient Episodic Memory for Continual Learning},
author={Lopez-Paz, David and Ranzato, Marc-Aurelio},
booktitle={Advances in Neural Information Processing Systems 30},
editor={I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages={6467--6476},
year={2017},
publisher={Curran Associates, Inc.},
url={http://papers.nips.cc/paper/7225-gradient-episodic-memory-for-continual-learning.pdf},
    keywords={Rehearsal, Influential}
}
@String(Lopez-Paz17="A model that alliviates CF via constrained optimization")


@inproceedings{shin2017continual,
  title={Continual learning with deep generative replay},
  author={Shin, Hanul and Lee, Jung Kwon and Kim, Jaehong and Kim, Jiwon},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2990--2999},
  year={2017},
    keywords={Generative Replay, Influential},
    url={https://arxiv.org/abs/1705.08690},
}
@String(shin2017continual="Introduces generative replay")

@ARTICLE{Goodfellow13,
   author={Goodfellow, I.~J. and Mirza, M. and Xiao, D. and Courville, A. and Bengio, Y.},
    title="An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks",
  journal={ArXiv e-prints},
archivePrefix="arXiv",
   eprint={1312.6211},
 primaryClass="stat.ML",
 keywords={Statistics - Machine Learning, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, Empirical Study, Influential},
     year={2013},
    month={dec},
   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1312.6211G},
  adsnote={Provided by the SAO/NASA Astrophysics Data System},
  url={https://arxiv.org/abs/1312.6211},
}
@String(Goodfellow13="Investigates CF in neural networks")

@misc{deLange2019continual,
    title={Continual learning: A comparative study on how to defy forgetting in classification tasks},
    author={Matthias De Lange and Rahaf Aljundi and Marc Masana and Sarah Parisot and Xu Jia and Ales Leonardis and Gregory Slabaugh and Tinne Tuytelaars},
    year={2019},
    eprint={1909.08383},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    keywords="Survey",
    url={https://arxiv.org/abs/1909.08383},
}
@String(deLange2019continual="Extensive empirical study of CL methods (in the multi-head setting)")

@article{Parisi18review,
title="Continual lifelong learning with neural networks: A review",
journal="Neural Networks",
volume="113",
pages="54 - 71",
year="2019",
issn="0893-6080",
doi="https://doi.org/10.1016/j.neunet.2019.01.012",
url="http://www.sciencedirect.com/science/article/pii/S0893608019300231",
author="German I. Parisi and Ronald Kemker and Jose L. Part and Christopher Kanan and Stefan Wermter",
keywords="Continual learning, Lifelong learning, Catastrophic forgetting, Developmental systems, Memory consolidation, Survey"
}
@String(Parisi18review="An extensive review of CL")

@inproceedings{hung2019compacting,
  title={Compacting, Picking and Growing for Unforgetting Continual Learning},
  author={Hung, Ching-Yi and Tu, Cheng-Hao and Wu, Cheng-En and Chen, Chien-Hung and Chan, Yi-Ming and Chen, Chu-Song},
  booktitle={Advances in Neural Information Processing Systems},
  pages={13647--13657},
  year={2019},
    keywords={Hybrid},
    url={https://arxiv.org/abs/1910.06562}
}
@String(hung2019compacting="Approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. All enforced in an iterative manner")

@article{Swaroop2019ImprovingAU,
  title={Improving and Understanding Variational Continual Learning},
  author={Siddharth Swaroop and Cuong V. Nguyen and Thang D. Bui and Richard E. Turner},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.02099},
    keywords={Regularization},
    url="https://arxiv.org/abs/1905.02099"
})
@String(Swaroop2019ImprovingAU="Improved results and interpretation of VCL.")


@inproceedings{Ebrahimi2020Uncertainty-guided,
title={Uncertainty-guided Continual Learning with Bayesian Neural Networks},
author={Sayna Ebrahimi and Mohamed Elhoseiny and Trevor Darrell and Marcus Rohrbach},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HklUCCVKDB}
}
@String(Ebrahimi2020Uncertainty-guided="Uses Bayes by Backprop for variational Continual Learning.")

@incollection{NIPS2019_8690,
title={Uncertainty-based Continual Learning with Adaptive Regularization},
author={Ahn, Hongjoon and Cha, Sungmin and Lee, Donggyu and Moon, Taesup},
booktitle={Advances in Neural Information Processing Systems 32},
editor={H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages={4394--4404},
year={2019},
publisher={Curran Associates, Inc.},
url={http://papers.nips.cc/paper/8690-uncertainty-based-continual-learning-with-adaptive-regularization.pdf},
    keywords={Regularization}
}
@String(NIPS2019_8690="Introduces VCL with uncertainty measured for neurons instead of weights.")

@misc{zeno2018task,
    title={Task Agnostic Continual Learning Using Online Variational Bayes},
    author={Chen Zeno and Itay Golan and Elad Hoffer and Daniel Soudry},
    year={2018},
    eprint={1803.10123},
    archivePrefix={arXiv},
    primaryClass={stat.ML},
    keywords={Regularization},
    url={https://arxiv.org/pdf/1803.10123.pdf}
}
@String(zeno2018task="Introduces an optimizer for CL that relies on closed form updates of mu and sigma of BNN; introduce label trick for class learning (single-head)")

@article{DBLP:journals/corr/abs-1902-09432,
  author   ={Jaehong Yoon and
               Saehoon Kim and
               Eunho Yang and
               Sung Ju Hwang},
  title    ={ORACLE: Order Robust Adaptive Continual Learning},
  journal  ={CoRR},
  volume   ={abs/1902.09432},
  year     ={2019},
  url      ={http://arxiv.org/abs/1902.09432},
  archivePrefix={arXiv},
  eprint   ={1902.09432},
  timestamp={Tue, 21 May 2019 18:03:36 +0200},
  biburl   ={https://dblp.org/rec/journals/corr/abs-1902-09432.bib},
  bibsource={dblp computer science bibliography, https://dblp.org},
    keywords={Dynamic Architecture}
}




@article{Rajasegaran2019Random,
  author   ={Jathushan Rajasegaran and
               Munawar Hayat and
               Salman H. Khan and
               Fahad Shahbaz Khan and
               Ling Shao},
  title    ={Random Path Selection for Incremental Learning},
  journal  ={CoRR},
  volume   ={abs/1906.01120},
  year     ={2019},
  url      ={http://arxiv.org/abs/1906.01120},
  archivePrefix={arXiv},
  eprint   ={1906.01120},
  timestamp={Fri, 06 Dec 2019 16:34:40 +0100},
  biburl   ={https://dblp.org/rec/journals/corr/abs-1906-01120.bib},
  bibsource={dblp computer science bibliography, https://dblp.org},
    keywords={Dynamic Architecture}
}
@String(Rajasegaran2019Random="Proposes a random path selection algorithm, called RPSnet, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing and reuse")




@inproceedings{He18,
title={Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation},
author={Xu He and Herbert Jaeger},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=B1al7jg0b},
    keywords={Regularization}
}
@String(He18="Conceptor-Aided Backprop (CAB): gradients are shielded by conceptors against degradation of previously learned tasks")

@inproceedings{Pentina15,
  title={Lifelong learning with non-iid tasks},
  author={Pentina, Anastasia and Lampert, Christoph H},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1540--1548},
  year={2015},
    keywords={}
}


@article{serra2018overcoming,
  title=	 {Overcoming Catastrophic Forgetting with Hard Attention to the Task},
  author=	 {Serra, Joan and Suris, Didac and Miron, Marius and Karatzoglou, Alexandros},
  journal  ={ICML},
  booktitle=	 {Proceedings of the 35th International Conference on Machine Learning},
  pages=	 {4548--4557},
  year=	 {2018},
  editor=	 {Dy, Jennifer and Krause, Andreas},
  volume=	 {80},
  series=	 {Proceedings of Machine Learning Research},
  address=	 {Stockholmsmässan, Stockholm Sweden},
  month=	 {10--15 Jul},
  publisher=	 {PMLR},
@inproceedings{ostapenko2019learning,
  title={Learning to remember: A synaptic plasticity driven framework for continual learning},
  author={Ostapenko, Oleksiy and Puscas, Mihai and Klein, Tassilo and Jahnichen, Patrick and Nabi, Moin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={11321--11329},
  year={2019}
}  pdf=	 {http://proceedings.mlr.press/v80/serra18a/serra18a.pdf},
  url=	 {http://proceedings.mlr.press/v80/serra18a.html},
    keywords={Regularization}
}
@String(serra2018overcoming="Introducing a hard attention idea with binary masks")

@inproceedings{chaudhry2018riemannian,
  title={Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence},
  author={Chaudhry, Arslan and Dokania, Puneet K and Ajanthan, Thalaiyasingam and Torr, Philip HS},
  booktitle={ECCV},
  year={2018},
    keywords={Regularization},
    url={https://arxiv.org/abs/1801.10112}
}
@String(chaudhry2018riemannian="Formalizes the shortcomings of multi-head evaluation, as well as the importance of replay in single-head setup. Presenting an improved version of EWC.")




@article{Aljundi17,
  author   ={Rahaf Aljundi and Francesca Babiloni and Mohamed Elhoseiny and Marcus Rohrbach and Tinne Tuytelaars},
  title    ={Memory Aware Synapses: Learning what (not) to forget},
  journal  ={CoRR},
  volume   ={abs/1711.09601},
  year     ={2017},
  url      ={http://arxiv.org/abs/1711.09601},
  archivePrefix={arXiv},
  eprint   ={1711.09601},
  timestamp={Mon, 13 Aug 2018 16:47:14 +0200},
  biburl   ={https://dblp.org/rec/bib/journals/corr/abs-1711-09601},
  bibsource={dblp computer science bibliography, https://dblp.org},
    keywords={Regularization}
}
@String(Aljundi17="Importance of parameter measured based on their contribution to change in the learned prediction function")



@inproceedings{nguyen2017variational,
    title={Variational Continual Learning},
    author={Cuong V. Nguyen and Yingzhen Li and Thang D. Bui and Richard E. Turner},
    booktitle={International Conference on Learning Representations},
    year={2018},
    keywords={Regularization},
    url={https://arxiv.org/abs/1710.10628}
}
@String(Nguyen17="Introduces the idea of using previous task's posterior as the new task's prior in a BNN.")


@inproceedings{schwarz2018progress,
  title={Progress \& compress: A scalable framework for continual learning},
  author={Schwarz, Jonathan and Luketina, Jelena and Czarnecki, Wojciech M and Grabska-Barwinska, Agnieszka and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia},
  booktitle={ICML},
  year={2018},
    keywords={Regularization},
    url={https://arxiv.org/abs/1805.06370}
}
@String(schwarz2018progress="A new P\&C architecture; online EWC for keeping the knowledge about the previous task, knowledge for keeping the knowledge about the current task (Multi-head setting, RL)")

@InProceedings{Zenke17,
  title=	 {Continual Learning Through Synaptic Intelligence},
  author=	 {Zenke, Friedeman and Poole, Ben and Ganguli, Surya },
  booktitle=	 {Proceedings of the 34th International Conference on Machine Learning},
  pages=	 {3987--3995},
  year=	 {2017},
  editor=	 {Doina Precup and Yee Whye Teh},
  volume=	 {70},
  series=	 {Proceedings of Machine Learning Research},
  address=	 {International Convention Centre, Sydney, Australia},
  month=	 {06--11 Aug},
  publisher=	 {PMLR},
  pdf=	 {http://proceedings.mlr.press/v70/zenke17a/zenke17a.pdf},
  url=	 {http://proceedings.mlr.press/v70/zenke17a.html},
    keywords={Regularization}
}
@String(Zenke17="Synaptic Intelligence (SI). Importance of parameter measured based on their contribution to change in the loss. ")

@article{Li17learning,
  title={Learning without forgetting},
  author={Li, Zhizhong and Hoiem, Derek},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2017},
  publisher={IEEE},
    keywords={Distillation},
    url={https://arxiv.org/abs/1606.09282}
}
@String(Li17learning="Functional regularization through distillation (keeping the output of the updated network on the new data close to the output of the old network on the new data)")


@misc{li2019continual,
    title={Continual Learning Using Bayesian Neural Networks},
    author={HongLin Li and Payam Barnaghi and Shirin Enshaeifar and Frieder Ganz},
    year={2019},
    eprint={1910.04112},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    keywords={}
}

@misc{
rosenfeld2018incremental,
title={Incremental Learning through Deep Adaptation},
author={Amir Rosenfeld and John K. Tsotsos},
year={2018},
url={https://openreview.net/forum?id=ryj0790hb},
    keywords={Dynamic Architecture}
}

@ARTICLE{Rusu16progressive,
   author={{Rusu}, A.~A. and {Rabinowitz}, N.~C. and {Desjardins}, G. and 
	{Soyer}, H. and {Kirkpatrick}, J. and {Kavukcuoglu}, K. and 
	{Pascanu}, R. and {Hadsell}, R.},
    title={Progressive Neural Networks},
  journal={ArXiv e-prints},
archivePrefix="arXiv",
   eprint={1606.04671},
 primaryClass="cs.LG",
 keywords={Computer Science - Learning, Dynamic Architecture},
     year=2016,
    month={jun},
   adsurl={http://adsabs.harvard.edu/abs/2016arXiv160604671R},
  adsnote={Provided by the SAO/NASA Astrophysics Data System},
   url={https://arxiv.org/abs/1606.04671}
}
@String(Rusu16progressive="Each task have a specific model connected to the previous ones")


@article{Rusu16sim2real,
  author    = {Andrei A. Rusu and
               Matej Vecerik and
               Thomas Roth{\"{o}}rl and
               Nicolas Heess and
               Razvan Pascanu and
               Raia Hadsell},
  title     = {Sim-to-Real Robot Learning from Pixels with Progressive Nets},
  journal   = {CoRR},
  volume    = {abs/1610.04286},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.04286},
  archivePrefix = {arXiv},
  eprint    = {1610.04286},
  timestamp = {Mon, 13 Aug 2018 16:48:16 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/RusuVRHPH16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{farajtabar2019orthogonal,
    title={Orthogonal Gradient Descent for Continual Learning},
    author={Mehrdad Farajtabar and Navid Azizan and Alex Mott and Ang Li},
    year={2019},
    eprint={1910.07104},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    keywords={Rehearsal},
url={https://arxiv.org/abs/1910.07104},
}
@String(farajtabar2019orthogonal="projecting the gradients from new tasks onto a subspace in which the neural network output on previous task does not change and the projected gradient is still in a useful direction for learning the new task")


@incollection{Aljundi2019Gradient,
title={Gradient based sample selection for online continual learning},
author={Aljundi, Rahaf and Lin, Min and Goujaud, Baptiste and Bengio, Yoshua},
booktitle={Advances in Neural Information Processing Systems 32},
editor={H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages={11816--11825},
year={2019},
publisher={Curran Associates, Inc.},
url={http://papers.nips.cc/paper/9354-gradient-based-sample-selection-for-online-continual-learning.pdf},
    keywords={Rehearsal}
}
@String(Aljundi2019Gradient="sample selection as a constraint reduction problem based on the constrained optimization view of continual learning")


@incollection{aljundi2019online,
title ={Online Continual Learning with Maximal Interfered Retrieval},
author={Aljundi, Rahaf and Caccia, Lucas and Belilovsky, Eugene and Caccia, Massimo and Lin, Min and Charlin, Laurent and Tuytelaars, Tinne},
booktitle={Advances in Neural Information Processing Systems 32},
editor={H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages={11849--11860},
year={2019},
publisher={Curran Associates, Inc.},
url={http://papers.nips.cc/paper/9357-online-continual-learning-with-maximal-interfered-retrieval.pdf},
keywords={Rehearsal}
}
@String(aljundi2019online="Controlled sampling of memories for replay to automatically rehearse on tasks currently undergoing the most forgetting")


@article{caccia2019online,
  title={Online Learned Continual Compression with Adaptative Quantization Module},
  author={Caccia, Lucas and Belilovsky, Eugene and Caccia, Massimo and Pineau, Joelle},
  journal={arXiv preprint arXiv:1911.08019},
  year={2019},
    keywords={Rehearsal},
    url={https://arxiv.org/abs/1911.08019}
}
@String(caccia2019online="Uses stacks of VQ-VAE modules to progressively compress the data stream, enabling better rehearsal")


@article{Ven2018GenerativeRW,
  title={Generative replay with feedback connections as a general strategy for continual learning},
  author={Michiel van der Ven and Andreas S. Tolias},
  journal={ArXiv},
  year={2018},
  volume={abs/1809.10635},
    keywords={Generative Replay},
    url={https://arxiv.org/abs/1809.10635}
}
@String(Ven2018GenerativeRW="smarter Generative Replay")

@incollection{Javed2019Meta,
title={Meta-Learning Representations for Continual Learning},
author={Javed, Khurram and White, Martha},
booktitle={Advances in Neural Information Processing Systems 32},
editor={H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages={1818--1828},
year={2019},
publisher={Curran Associates, Inc.},
url={http://papers.nips.cc/paper/8458-meta-learning-representations-for-continual-learning.pdf},
    keywords={Meta-Continual Learning}
}
@String(Javed2019Meta="Introduces Learns how to continually learn (OML) i.e. learns how to do online updates without forgetting.")

@misc{luo2019learning,
    title={Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling},
    author={Yadan Luo and Zi Huang and Zheng Zhang and Ziwei Wang and Mahsa Baktashmotlagh and Yang Yang},
    year={2019},
    eprint={1911.04695},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    keywords={Continual-Meta Learning},
    url={https://arxiv.org/abs/1911.04695}
}

@inproceedings{
Kurle2020Continual,
title={Continual Learning with Bayesian Neural Networks for Non-Stationary Data},
author={Richard Kurle and Botond Cseke and Alexej Klushyn and Patrick van der Smagt and Stephan Günnemann},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SJlsFpVtDB},
    keywords={Regularization}
}
@String(Kurle2020Continual="continual learning for non-stationary data using Bayesian neural networks and memory-based online variational Bayes")

@InProceedings{pmlr-v97-finn19a,
  title=	 {Online Meta-Learning},
  author=	 {Finn, Chelsea and Rajeswaran, Aravind and Kakade, Sham and Levine, Sergey},
  booktitle=	 {Proceedings of the 36th International Conference on Machine Learning},
  pages=	 {1920--1930},
  year=	 {2019},
  editor=	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume=	 {97},
  series=	 {Proceedings of Machine Learning Research},
  address=	 {Long Beach, California, USA},
  month=	 {09--15 Jun},
  publisher=	 {PMLR},
  pdf=	 {http://proceedings.mlr.press/v97/finn19a/finn19a.pdf},
  url=	 {http://proceedings.mlr.press/v97/finn19a.html},
    keywords={Continual-Meta Learning}
}
@String(pmlr-v97-finn19a="defines Online Meta-learning; propsoses Follow the Meta Leader (FTML) (~ Online MAML)")


@incollection{NIPS2019_9112,
title={Reconciling meta-learning and continual learning with online mixtures of tasks},
author={Jerfel, Ghassen and Grant, Erin and Griffiths, Tom and Heller, Katherine A},
booktitle={Advances in Neural Information Processing Systems 32},
editor={H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages={9119--9130},
year={2019},
publisher={Curran Associates, Inc.},
url={http://papers.nips.cc/paper/9112-reconciling-meta-learning-and-continual-learning-with-online-mixtures-of-tasks.pdf},
    keywords={Continual-Meta Learning}
}
@String(NIPS2019_9112="Meta-learns a tasks structure; continual adaptation via non-parametric prior")


@inproceedings{
        nagabandi2018deep,
        title={Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL},
        author={Anusha Nagabandi and Chelsea Finn and Sergey Levine},
        booktitle={International Conference on Learning Representations},
        year={2019},
        url={https://openreview.net/forum?id=HyxAfnA5tm},
        keywords={Continual-Meta Learning, Lifelong Reinforcement Learning}
}
@String(nagabandi2018deep="Formulates an online learning procedure that uses SGD to update model parameters, and an EM with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distribution")

@misc{rao2019continual,
    title={Continual Unsupervised Representation Learning},
    author={Dushyant Rao and Francesco Visin and Andrei A. Rusu and Yee Whye Teh and Razvan Pascanu and Raia Hadsell},
    year={2019},
    eprint={1910.14481},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    keywords={Generative Modeling},
    url={https://arxiv.org/pdf/1910.14481.pdf}
}
@String(rao2019continual="Introduces unsupervised continual learning (no task label and no task boundaries)")

@inproceedings{lesort2018generative,
  TITLE={Generative Models from the perspective of Continual Learning},
  AUTHOR={Lesort, Timoth{\'e}e and Caselles-Dupr{\'e}, Hugo and Garcia-Ortiz, Michael and Goudou, Jean-Fran{\c c}ois and Filliat, David},
  URL={https://hal.archives-ouvertes.fr/hal-01951954},
  BOOKTITLE={{IJCNN - International Joint Conference on Neural Networks}},
  ADDRESS={Budapest, Hungary},
  YEAR={2019},
  MONTH={Jul},
  PDF={https://hal.archives-ouvertes.fr/hal-01951954/file/_NIPS_CL_Workshop__Continual_learning_for_generative_models.pdf},
  HAL_ID={hal-01951954},
  keywords={"Generative Modeling", "Generative Replay"},
  HAL_VERSION={v1},
}
@String(lesort2018generative="Extensive evaluation of CL methods for generative modeling")

@article{ramapuram2017lifelong,
  title={Lifelong Generative Modeling},
  author={Ramapuram, Jason and Gregorova, Magda and Kalousis, Alexandros},
  journal={arXiv preprint arXiv:1705.09847},
  year={2017},
    keywords={Generative Modeling},
  url={https://arxiv.org/abs/1705.09847}
}
@String(Ramapuram17="first to focus on continual generative modeling (DGR's focus was still on continual supervised learning)")

@inproceedings{Alet2018ModularM,
  title={Modular meta-learning},
  author={Ferran Alet and Tom{\'a}s Lozano-P{\'e}rez and Leslie Pack Kaelbling},
  booktitle={CoRL},
  year={2018},
    keywords={}
}

@inproceedings{
toneva2018an,
title={An Empirical Study of Example Forgetting during Deep Neural Network Learning},
author={Mariya Toneva and Alessandro Sordoni and Remi Tachet des Combes and Adam Trischler and Yoshua Bengio and Geoffrey J. Gordon},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=BJlxm30cKm},
    keywords={}
}

@inproceedings{
Oswald2020Continual,
title={Continual learning with hypernetworks},
author={Johannes von Oswald and Christian Henning and João Sacramento and Benjamin F. Grewe},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SJgwNerKvB},
    keywords={Hybrid}
}
@String(Oswald2020Continual="Learning task-conditioned hypernetworks for continual learning as well as task embeddings; hypernetwors offers good model compression.")

@inproceedings{Mccloskey89,
  title={Catastrophic interference in connectionist networks: The sequential learning problem},
  author={McCloskey, Michael and Cohen, Neal J},
  booktitle={Psychology of learning and motivation},
  volume={24},
  pages={109--165},
  year={1989},
  publisher={Elsevier},
  keywords={Classics},
  url={https://www.sciencedirect.com/science/article/pii/S0079742108605368}
}
@String(Mccloskey89="Introduces CL and reveals the catastrophic forgetting problem")

@article{He2019TaskAC,
  title={Task Agnostic Continual Learning via Meta Learning},
  author={Xu He and Jakub Sygnowski and Alexandre Galashov and Andrei A. Rusu and Yee Whye Teh and Razvan Pascanu},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.05201},
  keywords={"Continual-Meta Learning"},
  url={https://arxiv.org/abs/1906.05201}
}
@String(He2019TaskAC="Introduces What & How framework; enables Task Agnostic CL with meta learned task inference")

@article{Harrison2019ContinuousMW,
  title={Continuous Meta-Learning without Tasks},
  author={James Harrison and Apoorva Sharma and Chelsea Finn and Marco Pavone},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.08866}
}

@article{van2019three,
  title={Three scenarios for continual learning},
  author={van de Ven, Gido M and Tolias, Andreas S},
  journal={arXiv preprint arXiv:1904.07734},
  year={2019},
  keywords={"Survey"},
  url={https://arxiv.org/abs/1904.07734},  
}
@String(van2019three="An extensive review of CL methods in three different scenarios (task-, domain-, and class-incremental learning)")

@inproceedings{lee2019overcoming,
  title={Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild},
  author={Lee, Kibok and Lee, Kimin and Shin, Jinwoo and Lee, Honglak},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={312--321},
  year={2019},
    keywords={Distillation},
   url={https://arxiv.org/abs/1903.12648},
}
@String(lee2019overcoming="Introducing global distillation loss and balanced finetuning; leveraging unlabeled data in the open world setting (Single-head setting)")

@inproceedings{Wu19Large,
  title={Large scale incremental learning},
  author={Wu, Yue and Chen, Yinpeng and Wang, Lijuan and Ye, Yuancheng and Liu, Zicheng and Guo, Yandong and Fu, Yun},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={374--382},
  year={2019},
    keywords={Distillation},
    url={https://arxiv.org/abs/1905.13260}
}
@String(Wu19Large="Introducing bias parameters to the last fully connected layer to resolve the data imbalance issue (Single-head setting)")

@inproceedings{hou2018lifelong,
  title={Lifelong learning via progressive distillation and retrospection},
  author={Hou, Saihui and Pan, Xinyu and Change Loy, Chen and Wang, Zilei and Lin, Dahua},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={437--452},
  year={2018},
    keywords={Distillation},
    url={https://arxiv.org/abs/1905.13260}
}
@String(hou2018lifelong="Introducing an expert of the current task in the knowledge distillation method (Multi-head setting)")

@inproceedings{castro2018end,
  title={End-to-end incremental learning},
  author={Castro, Francisco M and Marin-Jimenez, Manuel J and Guil, Nicolas and Schmid, Cordelia and Alahari, Karteek},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={233--248},
  year={2018},
    keywords={Distillation},
    url={https://arxiv.org/abs/1807.09536}
}
@String(castro2018end="Finetuning the last fully connected layer with a balanced dataset to resolve the data imbalance issue (Single-head setting)")

@inproceedings{rebuffi2017icarl,
  title={icarl: Incremental classifier and representation learning},
  author={Rebuffi, Sylvestre-Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph H},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2001--2010},
  year={2017},
    keywords={Distillation, Rehearsal},
  url={https://arxiv.org/abs/1611.07725}
}
@String(rebuffi2017icarl="Binary cross-entropy loss for representation learning & exemplar memory (or coreset) for replay (Single-head setting)")


@InProceedings{Hou_2019_CVPR,
author={Hou, Saihui and Pan, Xinyu and Loy, Chen Change and Wang, Zilei and Lin, Dahua},
title={Learning a Unified Classifier Incrementally via Rebalancing},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month={June},
year={2019},
    keywords={}
} 

@inproceedings{mallya2018packnet,
  title={Packnet: Adding multiple tasks to a single network by iterative pruning},
  author={Mallya, Arun and Lazebnik, Svetlana},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7765--7773},
  year={2018},
    keywords={}
}

@inproceedings{Mallya18Piggyback,
  title={Piggyback: Adapting a single network to multiple tasks by learning to mask weights},
  author={Mallya, Arun and Davis, Dillon and Lazebnik, Svetlana},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={67--82},
  year={2018},
    keywords={}
}

@inproceedings{wang17,
  title={Growing a brain: Fine-tuning by increasing model capacity},
  author={Wang, Yu-Xiong and Ramanan, Deva and Hebert, Martial},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2471--2480},
  year={2017},
    keywords={}
}



@inproceedings{finn2017model,
  title={Model-agnostic meta-learning for fast adaptation of deep networks},
  author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
  pages={1126--1135},
  year={2017},
  organization={JMLR.org},
    keywords={}
}

@article{raghu2019rapid,
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  author={Raghu, Aniruddh and Raghu, Maithra and Bengio, Samy and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1909.09157},
  year={2019},
    keywords={}
}
@inproceedings{kemker18fearnet,
title={FearNet: Brain-Inspired Model for Incremental Learning},
author={Ronald Kemker and Christopher Kanan},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=SJ1Xmf-Rb},
    keywords={}
}

@inproceedings{gepperth2016incremental,
  TITLE={{Incremental learning algorithms and applications}},
  AUTHOR={Gepperth, Alexander and Hammer, Barbara},
  URL={https://hal.archives-ouvertes.fr/hal-01418129},
  BOOKTITLE={{European Symposium on Artificial Neural Networks (ESANN)}},
  ADDRESS={Bruges, Belgium},
  YEAR={2016},
  PDF={https://hal.archives-ouvertes.fr/hal-01418129/file/article.pdf},
  HAL_ID={hal-01418129},
  HAL_VERSION={v1},
    keywords={}
}

@article{Soltoggio2019Born,
title="Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks",
journal="Neural Networks",
volume="108",
pages="48 - 67",
year="2018",
issn="0893-6080",
doi="https://doi.org/10.1016/j.neunet.2018.07.013",
url="http://www.sciencedirect.com/science/article/pii/S0893608018302120",
author="Andrea Soltoggio and Kenneth O. Stanley and Sebastian Risi",
keywords={"Survey"},
}
@String(Soltoggio2019Born=" ")

@inproceedings{aljundi2017expertGate,
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  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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}

@article{Silver13,
abstract={Lifelong Machine Learning Systems: Beyond Learning Algorithms},
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isbn={9781577356028},
journal={AAAI Spring Symposium Series},
keywords={AAAI Technical Report SS-13-05},
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pages={49--55},
title={{Lifelong Machine Learning Systems: Beyond Learning Algorithms}},
year={2013}
}


@inproceedings{Lee16,
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@article{Traore19DisCoRL,
  author   ={Ren{\'{e}} Traor{\'{e}} and
               Hugo Caselles{-}Dupr{\'{e}} and
               Timoth{\'{e}}e Lesort and
               Te Sun and
               Guanghang Cai and
               Natalia D{\'{\i}}az Rodr{\'{\i}}guez and
               David Filliat},
  title    ={DisCoRL: Continual Reinforcement Learning via Policy Distillation},
  journal  ={CoRR},
  volume   ={abs/1907.05855},
  year     ={2019},
  url      ={http://arxiv.org/abs/1907.05855},
  archivePrefix={arXiv},
  eprint   ={1907.05855},
  timestamp={Wed, 17 Jul 2019 10:27:36 +0200},
  biburl   ={https://dblp.org/rec/bib/journals/corr/abs-1907-05855},
  bibsource={dblp computer science bibliography, https://dblp.org},
    keywords={}
}

@inproceedings{Kalifou19,
  title={Continual Reinforcement Learning deployed in Real-life using PolicyDistillation and Sim2Real Transfer},
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  booktitle={ICML Workshop on Multi-Task and Lifelong Learning},
  year={2019},
    keywords={}
}

@inproceedings{Rios19,
 author={Rios, Amanda and Itti, Laurent},
 title={Closed-loop Memory GAN for Continual Learning},
 booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
 series={IJCAI'19},
 year={2019},
 isbn={978-0-9992411-4-1},
 location={Macao, China},
 pages={3332--3338},
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@INPROCEEDINGS{Hayes18NewMetrics,
author={T. L. Hayes and R. Kemker and N. D. Cahill and C. Kanan}, 
booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 
title={New Metrics and Experimental Paradigms for Continual Learning}, 
year={2018}, 
volume={}, 
number={}, 
pages={2112-21123}, 
keywords={Robots;Measurement;Training;Task analysis;Computational modeling;Neural networks;Data models}, 
doi={10.1109/CVPRW.2018.00273}, 
ISSN={2160-7516}, 
month={June}}

@article{Hayes18MemoryEfficient,
  author    = {Tyler L. Hayes and
               Nathan D. Cahill and
               Christopher Kanan},
  title     = {Memory Efficient Experience Replay for Streaming Learning},
  journal   = {CoRR},
  volume    = {abs/1809.05922},
  year      = {2018},
  url       = {http://arxiv.org/abs/1809.05922},
  archivePrefix = {arXiv},
  eprint    = {1809.05922},
  timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1809-05922},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Chen2018Lifelong,
  title={Lifelong Machine Learning},
  author={Chen, Zhiyuan and Liu, Bing},
  journal={Synthesis Lectures on Artificial Intelligence and Machine Learning},
  volume={12},
  number={3},
  pages={1--207},
  year={2018},
  publisher={Morgan \& Claypool Publishers},
  isbn={1681733021},
  keywords={"Book"}
}
@String(Chen2018Lifelong="Book on Continual Learning. Extensive review of older and newer algorithm; motivations and relation to other ML paradigms")


@article{LESORT2019Continual,
title="Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges",
journal="Information Fusion",
volume="58",
pages="52 - 68",
year="2020",
issn="1566-2535",
doi="https://doi.org/10.1016/j.inffus.2019.12.004",
url="http://www.sciencedirect.com/science/article/pii/S1566253519307377",
author="Timothée Lesort and Vincenzo Lomonaco and Andrei Stoian and Davide Maltoni and David Filliat and Natalia Díaz-Rodríguez",
keywords="Robotics, Reinforcement Learning, Deep Learning, Lifelong Learning, Continual Learning, Catastrophic Forgetting, Survey",
}
@String(Lesort19Continual="Extensive review of CL methods and their applications in robotics and framework proposition for continual learning")

@article{French99,
author={French, Robert M.},
doi={10.1016/S1364-6613(99)01294-2},
isbn={13646613},
issn={13646613},
journal={Trends in Cognitive Sciences},
number={4},
pages={128--135},
pmid={10322466},
title={Catastrophic forgetting in connectionist networks},
volume={3},
year={1999},
keywords={Classic},
url={https://www.sciencedirect.com/science/article/abs/pii/S1364661399012942}
}

@inproceedings{titsias2019functional,
  title={Functional Regularisation for Continual Learning with Gaussian Processes},
  author={Titsias, Michalis K and Schwarz, Jonathan and Matthews, Alexander G de G and Pascanu, Razvan and Teh, Yee Whye},
  booktitle={International Conference on Learning Representations},
  year={2019},
    keywords={Regularization},
    url={https://arxiv.org/abs/1901.11356}
}
@String(titsias2019functional="functional regularisation for Continual Learning: avoids forgetting a previous task by constructing and memorising an approximate posterior belief over the underlying task-specific function")


@article{Maltoni18,
  title={Continuous learning in single-incremental-task scenarios},
  author={Maltoni, Davide and Lomonaco, Vincenzo},
  journal={Neural Networks},
  volume={116},
  pages={56--73},
  year={2019},
  publisher={Elsevier},
  keywords={"Empirical Study"}
}


@article{Kemker2017Measuring,
   author={{Kemker}, R. and {McClure}, M. and {Abitino}, A. and {Hayes}, T. and 
	{Kanan}, C.},
    title="{Measuring Catastrophic Forgetting in Neural Networks}",
  journal={ArXiv e-prints},
archivePrefix="arXiv",
   eprint={1708.02072},
 primaryClass="cs.AI",
 keywords={Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning, Empirical Study},
     year=2017,
    month={aug},
   adsurl={http://adsabs.harvard.edu/abs/2017arXiv170802072K},
  adsnote={Provided by the SAO/NASA Astrophysics Data System}
}

@InProceedings{Lomonaco17,
  title=	 {{CORe50: a New Dataset and Benchmark for Continuous Object Recognition}},
  author=	 {Vincenzo Lomonaco and Davide Maltoni},
  booktitle=	 {Proceedings of the 1st Annual Conference on Robot Learning},
  pages=	 {17--26},
  year=	 {2017},
  editor=	 {Sergey Levine and Vincent Vanhoucke and Ken Goldberg},
  volume=	 {78},
  series=	 {Proceedings of Machine Learning Research},
  address=	 {},
  month=	 {13--15 Nov},
 keywords={Empirical Study, Dataset},
  publisher=	 {PMLR},
  pdf=	 {http://proceedings.mlr.press/v78/lomonaco17a/lomonaco17a.pdf},
  url=	 {http://proceedings.mlr.press/v78/lomonaco17a.html}
}

@inproceedings{lesort2018marginal,
  title={Marginal replay vs conditional replay for continual learning},
  author={Lesort, Timoth{\'e}e and Gepperth, Alexander and Stoian, Andrei and Filliat, David},
  booktitle={International Conference on Artificial Neural Networks},
  pages={466--480},
  year={2019},
  organization={Springer},
    url={https://arxiv.org/abs/1810.12069},
    keywords={Generative Replay, }
}
@String(lesort2018marginal="Extensive evaluation of generative replay methods")

@article{Collet15,
  title={Herbdisc: Towards lifelong robotic object discovery},
  author={Collet, Alvaro and Xiong, Bo and Gurau, Corina and Hebert, Martial and Srinivasa, Siddhartha S},
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  volume={34},
  number={1},
  pages={3--25},
  year={2015},
  publisher={SAGE Publications Sage UK: London, England},
    keywords={Robotics}
}


@article{beaulieu2020learning,
  title={Learning to Continually Learn},
  author={Beaulieu, Shawn and Frati, Lapo and Miconi, Thomas and Lehman, Joel and Stanley, Kenneth O and Clune, Jeff and Cheney, Nick},
  journal={arXiv preprint arXiv:2002.09571},
  year={2020},
    keywords={Meta-Continual Learning},
    url={https://arxiv.org/abs/2002.09571}
}
@String(beaulieu2020learning="Follow-up of OML. Meta-learns an activation-gating function instead.")

@article{spigler2019meta,
  title={Meta-learnt priors slow down catastrophic forgetting in neural networks},
  author={Spigler, Giacomo},
  journal={arXiv preprint arXiv:1909.04170},
  year={2019},
    keywords={Meta-Continual Learning},
    url={https://arxiv.org/pdf/1909.04170.pdf}
}
@String(spigler2019meta="Learning MAML in a Meta continual learning way slows down forgetting")


@article{riemer2018learning,
  title={Learning to learn without forgetting by maximizing transfer and minimizing interference},
  author={Riemer, Matthew and Cases, Ignacio and Ajemian, Robert and Liu, Miao and Rish, Irina and Tu, Yuhai and Tesauro, Gerald},
  journal={arXiv preprint arXiv:1810.11910},
  year={2018},
    keywords={Meta-Continual Learning},
    url={https://arxiv.org/abs/1810.11910}
}

@inproceedings{rolnick2019experience,
  title={Experience replay for continual learning},
  author={Rolnick, David and Ahuja, Arun and Schwarz, Jonathan and Lillicrap, Timothy and Wayne, Gregory},
  booktitle={Advances in Neural Information Processing Systems},
  pages={348--358},
  year={2019},
    keywords={Reinforcement, Rehearsal},
  url={https://arxiv.org/abs/1811.11682}
}



@article{caccia2020online,
  title={Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning},
  author={Caccia, Massimo and Rodriguez, Pau and Ostapenko, Oleksiy and Normandin, Fabrice and Lin, Min and Caccia, Lucas and Laradji, Issam and Rish, Irina and Lacoste, Alexandre and Vazquez, David and others},
  journal={CVPR2020 - Workshop on Continual Learning},
  year={2020},
  url={https://arxiv.org/abs/2003.05856},
  keywords={Continual-Meta Learning, Setting}
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@String(caccia2020online="Proposes a new approach to CL evaluation more aligned with real-life applications, bringing CL closer to Online Learning and Open-World learning")


@inproceedings{
Yoon2020Scalable,
title={Scalable and Order-robust Continual Learning with Additive Parameter Decomposition},
author={Jaehong Yoon and Saehoon Kim and Eunho Yang and Sung Ju Hwang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=r1gdj2EKPB}
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@inproceedings{
Adel2020Continual,
title={Continual Learning with Adaptive Weights (CLAW)},
author={Tameem Adel and Han Zhao and Richard E. Turner},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Hklso24Kwr}
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@inproceedings{
Lee2020A,
title={A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning},
author={Soochan Lee and Junsoo Ha and Dongsu Zhang and Gunhee Kim},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SJxSOJStPr}
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@inproceedings{
Li2020Compositional,
title={Compositional Language Continual Learning},
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booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rklnDgHtDS}
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@inproceedings{
Wen2020BatchEnsemble:,
title={BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning},
author={Yeming Wen and Dustin Tran and Jimmy Ba},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Sklf1yrYDr}
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@inproceedings{
Yoo2020SNOW:,
title={SNOW: Subscribing to Knowledge via Channel Pooling for Transfer & Lifelong Learning of Convolutional Neural Networks},
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booktitle={International Conference on Learning Representations},
year={2020},
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@inproceedings{
sun2020lamal,
title={LAMAL: LAnguage Modeling Is All You Need for Lifelong Language Learning},
author={Fan-Keng Sun and Cheng-Hao Ho and Hung-Yi Lee},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Skgxcn4YDS},
keywords={Applications}
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@inproceedings{
Asghar2020Progressive,
title={Progressive Memory Banks for Incremental Domain Adaptation},
author={Nabiha Asghar and Lili Mou and Kira A. Selby and Kevin D. Pantasdo and Pascal Poupart and Xin Jiang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=BkepbpNFwr}
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@inproceedings{
Li2020Compositional,
title={Compositional Language Continual Learning},
author={Yuanpeng Li and Liang Zhao and Kenneth Church and Mohamed Elhoseiny},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rklnDgHtDS},
keywords={Applications, Setting}
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@String(Li2020Compositional="method for compositional continual learning of sequence-to-sequence models")


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@inproceedings{belouadah2020scail,
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@misc{she2019openlorisobject,
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@misc{shi2019ready,
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@inproceedings{cavallari2017fly,
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@INPROCEEDINGS{Craye15,
author={C. {Craye} and D. {Filliat} and J. {Goudou}},
booktitle={2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)},
title={Exploration strategies for incremental learning of object-based visual saliency},
year={2015},
volume={},
number={},
pages={13-18},
keywords={image classification;image colour analysis;indoor environment;learning (artificial intelligence);object detection;object-based visual saliency;indoor environment;task-specific visual saliency;environment exploration mechanism;geometrical manner;salient element discovery;classifier training;salient object prediction;large-scale visual features;intrinsic motivation;RGB-D images;indoor object detection;incremental learning;Visualization;Uncertainty;Training;Vegetation;Indoor environments;Robot sensing systems},
doi={10.1109/DEVLRN.2015.7346099},
ISSN={},
month={Aug},}


@InProceedings{Dhar19,
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@article{Jung16,
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  year      = {2016},
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@article{Velez17,
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@inproceedings{lee2017overcoming,
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@INPROCEEDINGS{camoriano17a,
author={R. {Camoriano} and G. {Pasquale} and C. {Ciliberto} and L. {Natale} and L. {Rosasco} and G. {Metta}},
booktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)},
title={Incremental robot learning of new objects with fixed update time},
year={2017},
volume={},
number={},
pages={3207-3214},
keywords={image classification;learning (artificial intelligence);least squares approximations;object recognition;robot vision;incremental robot learning;fixed update time;object recognition;lifelong learning;robotic agent learning;incremental variant;regularized least squares for classification;RLSC algorithm;machine learning benchmark dataset;robotic setting;Training;Robots;Computational modeling;Object recognition;Machine learning algorithms;Training data;Standards},
doi={10.1109/ICRA.2017.7989364},
ISSN={},
month={May},}


@inproceedings{Camoriano16,
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  added-at = {2015-01-27T15:35:24.000+0100},
  author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.},
  biburl = {https://www.bibsonomy.org/bibtex/263070703e6bb812852cca56574aed093/hotho},
  booktitle = {AAAI},
  description = {Papers by William W. Cohen},
  interhash = {52d0d71f6f5b332dabc1412f18e3a93d},
  intrahash = {63070703e6bb812852cca56574aed093},
  keywords = {learning nell ontology semantic toread},
  note = {: Never-Ending Learning in AAAI-2015},
  timestamp = {2015-01-27T15:35:24.000+0100},
  title = {Never-Ending Learning},
  url = {http://www.cs.cmu.edu/~wcohen/pubs.html},
  year = 2015
}


@article{aljundi2019continual,
  title={Continual Learning in Neural Networks},
  author={Aljundi, Rahaf},
  journal={arXiv preprint arXiv:1910.02718},
  year={2019},
  url={https://arxiv.org/abs/1910.02718},
  keywords={Thesis}
}


@inproceedings{kim2018keep,
  title={Keep and learn: Continual learning by constraining the latent space for knowledge preservation in neural networks},
  author={Kim, Hyo-Eun and Kim, Seungwook and Lee, Jaehwan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={520--528},
  year={2018},
  organization={Springer}
}

@article {FleschE10313,
	author = {Flesch, Timo and Balaguer, Jan and Dekker, Ronald and Nili, Hamed and Summerfield, Christopher},
	title = {Comparing continual task learning in minds and machines},
	volume = {115},
	number = {44},
	pages = {E10313--E10322},
	year = {2018},
	doi = {10.1073/pnas.1800755115},
	publisher = {National Academy of Sciences},
	abstract = {Humans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this {\textquotedblleft}continual learning{\textquotedblright} is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art neural networks while they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that blocked one task at a time, especially when they had a prior bias to represent stimuli in a way that encouraged task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.Humans can learn to perform multiple tasks in succession over the lifespan ({\textquotedblleft}continual{\textquotedblright} learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form {\textquotedblleft}factorized{\textquotedblright} representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/115/44/E10313},
	eprint = {https://www.pnas.org/content/115/44/E10313.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}



@article{Li19,
  author    = {Xilai Li and
               Yingbo Zhou and
               Tianfu Wu and
               Richard Socher and
               Caiming Xiong},
  title     = {Learn to Grow: {A} Continual Structure Learning Framework for Overcoming
               Catastrophic Forgetting},
  journal   = {CoRR},
  volume    = {abs/1904.00310},
  year      = {2019},
  url       = {http://arxiv.org/abs/1904.00310},
  archivePrefix = {arXiv},
  eprint    = {1904.00310},
  timestamp = {Wed, 24 Apr 2019 12:21:25 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1904-00310},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gepperthICANN,
title={Catastrophic forgetting: still a problem for DNNs},
year=2018,
booktitle={International Conference on Artificial Neural Networks ({ICANN})},
author={Pf\"ulb, B and Gepperth, A and Abdullah, S and Krawczyk, A},
}

@inproceedings{Rahaf2019selfless,
title={Selfless Sequential Learning},
author={Rahaf Aljundi and Marcus Rohrbach and Tinne Tuytelaars},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bkxbrn0cYX},
}


@inproceedings{liu2018rotate,
  title={Rotate your networks: Better weight consolidation and less catastrophic forgetting},
  author={Liu, Xialei and Masana, Marc and Herranz, Luis and Van de Weijer, Joost and Lopez, Antonio M and Bagdanov, Andrew D},
  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
  pages={2262--2268},
  year={2018},
  organization={IEEE}
}

@InProceedings{Ruvolo13,
  title = 	 {{ELLA}: An Efficient Lifelong Learning Algorithm},
  author = 	 {Paul Ruvolo and Eric Eaton},
  booktitle = 	 {Proceedings of the 30th International Conference on Machine Learning},
  pages = 	 {507--515},
  year = 	 {2013},
  editor = 	 {Sanjoy Dasgupta and David McAllester},
  volume = 	 {28},
  number =       {1},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Atlanta, Georgia, USA},
  month = 	 {17--19 Jun},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v28/ruvolo13.pdf},
  url = 	 {http://proceedings.mlr.press/v28/ruvolo13.html},
}

@inproceedings{Pentina14,
  title={A {PAC}-Bayesian bound for lifelong learning},
  author={Pentina, Anastasia and Lampert, Christoph},
  booktitle={International Conference on Machine Learning},
  pages={991--999},
  year={2014}
}

@article{Lee17Toward,
  title={Toward Continual Learning for Conversational Agents},
  author={Lee, Sungjin},
  journal={arXiv preprint arXiv:1712.09943},
  year={2017}
}



@article{Santos-Diez10,
  title={Lifelong learning by evolution in robotics: bridging the gap from theory to reality},
  author={Santos-Diez, B and Bellas, F and Fai{\~n}a, A and Duro, RJ},
  year={2010},
  publisher={Citeseer},
  keywords={Robotics}
}

@article{ren2017life,
  title={Life-long learning based on dynamic combination model},
  author={Ren, Boya and Wang, Hongzhi and Li, Jianzhong and Gao, Hong},
  journal={Applied Soft Computing},
  volume={56},
  pages={398--404},
  year={2017},
  publisher={Elsevier}
}


@inproceedings{Nehmzow09,
  title={A proposal for continual learning in robotics},
  author={Nehmzow, Ulrich and Riano, Lorenzo and Londonderry, UK},
  year={2009}
}

@inproceedings{diethe2018continual,
  title={Continual Learning in Practice},
  author={Diethe, Tom and Borchert, Tom and Thereska, Eno and Pigem, Borja de Balle and Lawrence, Neil},
  booktitle={NeurIPS Continual Learning Workshop},
  keywords={Influential, Dynamic Architecture},
  url={https://arxiv.org/abs/1903.05202},
  year={2018}
}
@String(diethe2018continual="Proposes a reference architecture for a continual learning system")

@inproceedings{chen2018facilitating,
  title={Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients},
  author={Chen, Yu and Diethe, Tom and Lawrence, Neil},
  booktitle={NeurIPS workshop on Continual Learning},
  keywords={Regularization},
  url={https://arxiv.org/abs/1904.10644},
  year={2018}
}
@String(chen2018facilitating="Improves on VCL")

@article{knoblauch2020optimal,
  title={Optimal Continual Learning has Perfect Memory and is NP-hard},
  author={Knoblauch, Jeremias and Husain, Hisham and Diethe, Tom},
  journal={arXiv preprint arXiv:2006.05188},
  year={2020}
}

@article{gupta2020neural,
  title={Neural Topic Modeling with Continual Lifelong Learning},
  author={Gupta, Pankaj and Chaudhary, Yatin and Runkler, Thomas and Sch{\"u}tze, Hinrich},
  journal={arXiv preprint arXiv:2006.10909},
  year={2020}
}

@article{yoon2020xtarnet,
  title={XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning},
  author={Yoon, Sung Whan and Kim, Do-Yeon and Seo, Jun and Moon, Jaekyun},
  journal={arXiv preprint arXiv:2003.08561},
  year={2020}
}

@inproceedings{ren2019incremental,
  title={Incremental few-shot learning with attention attractor networks},
  author={Ren, Mengye and Liao, Renjie and Fetaya, Ethan and Zemel, Richard},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5275--5285},
  year={2019}
}

@inproceedings{tao2020few,
  title={Few-Shot Class-Incremental Learning},
  author={Tao, Xiaoyu and Hong, Xiaopeng and Chang, Xinyuan and Dong, Songlin and Wei, Xing and Gong, Yihong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12183--12192},
  year={2020}
}

@inproceedings{perez2020incremental,
  title={Incremental Few-Shot Object Detection},
  author={Perez-Rua, Juan-Manuel and Zhu, Xiatian and Hospedales, Timothy M and Xiang, Tao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13846--13855},
  year={2020}
}

@inproceedings{abati2020conditional,
  title={Conditional Channel Gated Networks for Task-Aware Continual Learning},
  author={Abati, Davide and Tomczak, Jakub and Blankevoort, Tijmen and Calderara, Simone and Cucchiara, Rita and Bejnordi, Babak Ehteshami},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3931--3940},
  year={2020}
}

 @InProceedings{He_2020_CVPR,
author = {He, Jiangpeng and Mao, Runyu and Shao, Zeman and Zhu, Fengqing},
title = {Incremental Learning in Online Scenario},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
} 

@inproceedings{rajasegaran2020itaml,
  title={iTAML: An Incremental Task-Agnostic Meta-learning Approach},
  author={Rajasegaran, Jathushan and Khan, Salman and Hayat, Munawar and Khan, Fahad Shahbaz and Shah, Mubarak},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13588--13597},
  year={2020}
}

@inproceedings{wang2007background,
  title={Background subtraction using incremental subspace learning},
  author={Wang, Lu and Wang, Lei and Wen, Ming and Zhuo, Qing and Wang, Wenyuan},
  booktitle={2007 IEEE International Conference on Image Processing},
  volume={5},
  pages={V--45},
  year={2007},
  organization={IEEE}
}

@InProceedings{Meng_2020_CVPR,
author = {Meng, Qier and Shin'ichi, Satoh},
title = {ADINet: Attribute Driven Incremental Network for Retinal Image Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
} 

@inproceedings{yu2020semantic,
  title={Semantic drift compensation for class-incremental learning},
  author={Yu, Lu and Twardowski, Bartlomiej and Liu, Xialei and Herranz, Luis and Wang, Kai and Cheng, Yongmei and Jui, Shangling and Weijer, Joost van de},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6982--6991},
  year={2020}
}

@inproceedings{zhao2020maintaining,
  title={Maintaining Discrimination and Fairness in Class Incremental Learning},
  author={Zhao, Bowen and Xiao, Xi and Gan, Guojun and Zhang, Bin and Xia, Shu-Tao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13208--13217},
  year={2020}
}

@inproceedings{liu2020mnemonics,
  title={Mnemonics Training: Multi-Class Incremental Learning without Forgetting},
  author={Liu, Yaoyao and Su, Yuting and Liu, An-An and Schiele, Bernt and Sun, Qianru},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12245--12254},
  year={2020}
}