CS762 Fall 2022

Tentative Schedule (Subject to Change)

Week	Date	Topic	Reading materials	Assignments
1	Thursday, September 8	Course overview and introduction		Sign up for paper presentations and scribes (link to Google sheet)
2	Tuesday, September 13	Evolution of Neural Architecture (lecture)	D2L Book Chapter 7 & 10
2	Thursday, September 15	Evolution of Neural Architecture II	CNNs ImageNet Classification with Deep Convolutional Neural Networks Deep Residual Learning for Image Recognition Densely Connected Convolutional Networks A ConvNet for the 2020s (deep dive)
3	Tuesday, September 20	Evolution of Neural Architecture III	Transformers Attention Is All You Need BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (deep dive) Hierarchical Vision Transformer using Shifted Windows HOW DO VISION TRANSFORMERS WORK?
3	Thursday, September 22	Trustworthy Deep Learning I (lecture)	Goodfellow-Bengio-Courville Chapter 7.5, 7.13	Team registration (link to Google sheet)
4	Tuesday, September 27	Trustworthy Deep Learning II	Out-of-distribution Reliability Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Energy-based Out-of-distribution Detection VOS: Learning What You Don’t Know by Virtual Outlier Synthesis (deep dive)
4	Thursday, September 29	Trustworthy Deep Learning III	Adversarial Robustness Intriguing properties of neural networks Towards Deep Learning Models Resistant to Adversarial Attacks Robust Physical-World Attacks on Deep Learning Models Unlabeled Data Improves Adversarial Robustness Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search Understanding and Mitigating the Tradeoff Between Robustness and Accuracy (deep dive)
5	Tuesday, October 4	No class (use the time to discuss the project proposal with team)		Project proposal submission deadline on October 7 Midnight (download proposal latex template here)
5	Thursday, October 6	Trustworthy Deep Learning IV	Fairness and Group Robustness Equality of Opportunity in Supervised Learning (deep dive) An investigation of why overparameterization exacerbates spurious correlations Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
6	Tuesday, October 11	Meetings with Instructor to Review Project Proposals (optional)		Signup sheet for 15-min slot (link to Google sheet)
8	Thursday, October 13	Interpretable Deep Learning I (Lecture)
9	Tuesday, October 18	Interpretable Deep Learning II	Learning Deep Features for Discriminative Localization (CAM) Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Towards A Rigorous Science of Interpretable Machine Learning "Why Should I Trust You?": Explaining the Predictions of Any Classifier (deep dive)
10	Thursday, October 20	Deep Learning Generalization and Theory I
10	Tuesday, October 25	Deep Learning Generalization and Theory II	Exploring Generalization in Deep Learning Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks Visualizing the Loss Landscape of Neural Nets Reconciling modern machine-learning practice and the classical bias–variance trade-off Deep learning: a statistical viewpoint Deep Double Descent: Where Bigger Models and More Data Hurt (deep dive)
11	Thursday, October27	Deep Learning Generalization and Theory III	Generalization under distributional shift Invariant Risk Minimization Self-training Avoids Using Spurious Features Under Domain Shift Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution(deep dive)
6	Tuesday, November 1	Learning with Less Supervision I (Lecture)
6	Thursday, November 3	Learning with Less Supervision II	Large-scale pre-training Exploring the Limits of Weakly Supervised Pretraining Big Transfer (BiT): General Visual Representation Learning Rethinking Pre-training and Self-training Learning Transferable Visual Models From Natural Language Supervision (deep dive)
7	Tuesday, Novermber 8	Learning with Less Supervision III	Self-supervised learning A Simple Framework for Contrastive Learning of Visual Representations Supervised Contrastive Learning Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere (deep dive) Understanding self-supervised Learning Dynamics without Contrastive Pairs
7	Thursday, November 10	Learning with Less Supervision IV	Data Augmentation Automating the Art of Data Augmentation: Overview Automating the Art of Data Augmentation: Practice Automating the Art of Data Augmentation: Theory Automating the Art of Data Augmentation: New Direction Unsupervised Data Augmentation for Consistency Training On the Generalization Effects of Linear Transformations in Data Augmentation (deep dive)
8	Tuesday, November 15	Continual / Lifelong Learning I (Lecture)
8	Thursday, November 17	Continual / Lifelong Learning II	iCaRL - Incremental Classifier and Representation Learning LwF-Learning without Forgetting Overcoming catastrophic forgetting in neural networks Dark Experience for General Continual Learning: a Strong, Simple Baseline (deep dive)
9	Tuesday, November 22	Continual / Lifelong Learning III	Representational Continuity for Unsupervised Continual Learning (deep dive) Self-Supervised Models are Continual Learners
9	Thursday, November 24	No class (Thanksgiving)
10	Tuesday, November 29	Deep Generative Model I (Lecture)	Goodfellow-Bengio-Courville Chapter 20
10	Thursday, December 1	Deep Generative Model II	Generative Adversarial Networks InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Stacked Generative Adversarial Networks Large Scale GAN Training for High Fidelity Natural Image Synthesis Wasserstein GAN (deep dive) Self-Attention Generative Adversarial Networks
11	Tuesday, December 6	Deep Generative Model III	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks StarGAN v2: Diverse Image Synthesis for Multiple Domains A Style-Based Generator Architecture for Generative Adversarial Networks Hierarchical Text-Conditional Image Generation with CLIP Latents (deep dive)
11	Thursday, December 8	Final project presentation (Part I)
12	Tuesday, December 13	Final project presentation (Part II)
	Wednesday, December 21	Final project written report due (by end of the day)