Update assignment3.md

Author: AntheaLi

assignments/2021/assignment3.md

@@ -18,13 +18,13 @@ This assignment is due on **Tuesday, May 25 2021** at 11:59pm PDT.
 - [Setup](#setup)
 - [Goals](#goals)
   - [Google Colaboratory](#option-a-google-colaboratory-recommended)
-- [Q1: Image Captioning with Vanilla RNNs (29 points)](#q1-image-captioning-with-vanilla-rnns-29-points)
-- [Q2: Image Captioning with LSTMs (23 points)](#q2-image-captioning-with-lstms-23-points)
-- [Q3: Image Captioning with Transformers ( points)](#q3-image-captioning-with-transformers-18-points)
-- [Q4: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)](#q3-network-visualization-saliency-maps-class-visualization-and-fooling-images-15-points)
-- [Q5: Generative Adversarial Networks (15 points)](#q5-generative-adversarial-networks-15-points)
-- [Q6: Self-Supervised Learning for Image Classification (16 points)](#q6-self-supervised-learning-16-points)
-- [Optional: Style Transfer (15 points)](#optional-style-transfer-15-points)
+- [Q1: Image Captioning with LSTMs (23 points)](#q1-image-captioning-with-lstms-23-points)
+- [Q2: Image Captioning with Transformers (18 points)](#q2-image-captioning-with-transformers-18-points)
+- [Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)](#q3-network-visualization-saliency-maps-class-visualization-and-fooling-images-15-points)
+- [Q4: Generative Adversarial Networks (15 points)](#q4-generative-adversarial-networks-15-points)
+- [Q5: Self-Supervised Learning for Image Classification (15 points)](#q5-self-supervised-learning-15-points)
+- [Optional (Extra Credit): Image Captioning with Vanilla RNNs (tbd points)](#optional-image-captioning-with-vanilla-rnns-29-points)
+- [Optional (Extra Credit): Style Transfer (tbd points)](#optional-style-transfer-15-points)
 - [Submitting your work](#submitting-your-work)
 
 
@@ -56,31 +56,31 @@ The goals of this assignment are as follows:
 
 **You will use PyTorch for the majority of this homework.**
 
-### Q1: Image Captioning with Vanilla RNNs (29 points)
-
-The notebook `RNN_Captioning.ipynb` will walk you through the implementation of vanilla recurrent neural networks and apply them to image captioning on COCO.
-
-### Q2: Image Captioning with LSTMs (23 points)
+### Q1: Image Captioning with LSTMs (23 points)
 
 The notebook `LSTM_Captioning.ipynb` will walk you through the implementation of Long-Short Term Memory (LSTM) RNNs and apply them to image captioning on COCO.
 
-### Q3: Image Captioning with Transformers (18 points)
+### Q2: Image Captioning with Transformers (18 points)
 
 The notebook `Transformer_Captioning.ipynb` will walk you through the implementation of a Transformer model and apply it to image captioning on COCO. **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**
 
-### Q4: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)
+### Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)
 
 The notebook `Network_Visualization.ipynb` will introduce the pretrained SqueezeNet model, compute gradients with respect to images, and use them to produce saliency maps and fooling images.
 
-### Q5: Generative Adversarial Networks (15 points)
+### Q4: Generative Adversarial Networks (15 points)
 
 In the notebook `Generative_Adversarial_Networks.ipynb` you will learn how to generate images that match a training dataset and use these models to improve classifier performance when training on a large amount of unlabeled data and a small amount of labeled data. **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**
 
-### Q6: Self-Supervised Learning (16-points)
+### Q5: Self-Supervised Learning (16-points)
 
-In the notebook `Self_Supervised_Learning.ipynb`, you will learn how to ... **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**
+In the notebook `Self_Supervised_Learning.ipynb`, you will learn how to leverage self-supervised pretraining to obtain better performance on image classification task **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**
+
+### Optional (Extra Credit): Image Captioning with Vanilla RNNs (tbd points)
+
+The notebook `RNN_Captioning.ipynb` will walk you through the implementation of vanilla recurrent neural networks and apply them to image captioning on COCO.
 
-### Optional: Style Transfer (15 points)
+### Optional (Extra Credit): Style Transfer (tbd points)
 
 In the notebook `Style_Transfer.ipynb`, you will learn how to create images with the content of one image but the style of another.