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+---
+layout: page
+title: Assignment 3
+mathjax: true
+permalink: /assignments2024/assignment3/
+---
+
+<span style="color:red">This assignment is due on **Tuesday, May 30 2023** at 11:59pm PST.</span>
+
+Starter code containing Colab notebooks can be [downloaded here]({{site.hw_3_colab}}).
+
+- [Setup](#setup)
+- [Goals](#goals)
+- [Q1: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images](#q1-network-visualization-saliency-maps-class-visualization-and-fooling-images)
+- [Q2: Image Captioning with Vanilla RNNs](#q2-image-captioning-with-vanilla-rnns)
+- [Q3: Image Captioning with Transformers](#q3-image-captioning-with-transformers)
+- [Q4: Generative Adversarial Networks](#q4-generative-adversarial-networks)
+- [Q5: Self-Supervised Learning for Image Classification](#q5-self-supervised-learning-for-image-classification)
+- [Extra Credit: Image Captioning with LSTMs](#extra-credit-image-captioning-with-lstms-5-points)
+- [Submitting your work](#submitting-your-work)
+
+### Setup
+
+Please familiarize yourself with the [recommended workflow]({{site.baseurl}}/setup-instructions/#working-remotely-on-google-colaboratory) before starting the assignment. You should also watch the Colab walkthrough tutorial below.
+
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/DsGd2e9JNH4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+**Note**. Ensure you are periodically saving your notebook (`File -> Save`) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
+
+While we don't officially support local development, we've added a <b>requirements.txt</b> file that you can use to setup a virtual env.
+
+Once you have completed all Colab notebooks **except `collect_submission.ipynb`**, proceed to the [submission instructions](#submitting-your-work).
+
+### Goals
+
+In this assignment, you will implement language networks and apply them to image captioning on the COCO dataset. Then you will train a Generative Adversarial Network to generate images that look like a training dataset. Finally, you will be introduced to self-supervised learning to automatically learn the visual representations of an unlabeled dataset.
+
+The goals of this assignment are as follows:
+
+- Understand and implement RNN and Transformer networks. Combine them with CNN networks for image captioning.
+- Understand how to train and implement a Generative Adversarial Network (GAN) to produce images that resemble samples from a dataset.
+- Understand how to leverage self-supervised learning techniques to help with image classification tasks.
+
+**You will use PyTorch for the majority of this homework.**
+
+### Q1: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images
+
+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.
+
+### Q2: Image Captioning with Vanilla RNNs
+
+The notebook `RNN_Captioning.ipynb` will walk you through the implementation of vanilla recurrent neural networks and apply them to image captioning on COCO.
+
+### Q3: Image Captioning with Transformers
+
+The notebook `Transformer_Captioning.ipynb` will walk you through the implementation of a Transformer model and apply it to image captioning on COCO.
+
+### Q4: Generative Adversarial Networks 
+
+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`.**
+
+### Q5: Self-Supervised Learning for Image Classification 
+
+In the notebook `Self_Supervised_Learning.ipynb`, you will learn how to leverage self-supervised pretraining to obtain better performance on image classification tasks. **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**
+
+### Extra Credit: Image Captioning with LSTMs
+
+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.
+
+### Submitting your work
+
+**Important**. Please make sure that the submitted notebooks have been run and the cell outputs are visible.
+
+Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:
+
+**1.** Open `collect_submission.ipynb` in Colab and execute the notebook cells.
+
+This notebook/script will:
+
+* Generate a zip file of your code (`.py` and `.ipynb`) called `a3_code_submission.zip`.
+* Convert all notebooks into a single PDF file called `a3_inline_submission.pdf`.
+
+If your submission for this step was successful, you should see the following display message:
+
+`### Done! Please submit a3_code_submission.zip and a3_inline_submission.pdf to Gradescope. ###`
+
+**2.** Submit the PDF and the zip file to Gradescope.
+
+Remember to download `a3_code_submission.zip` and `a3_inline_submission.pdf` locally before submitting to Gradescope.