GANs: What the Generator and Discriminator Learn | CMU 10-423 Lecture 6
A classifier asks which class an image belongs to.
#gan
7 postsGenerative Models I — What Autoregressive Models, VAEs, and GANs Each Optimize | Stanford CS231N Lecture 13
The first generative-models lecture of CS231N Spring 2026 starts by pinning down the difference between a discriminative model, which learns p(y|x), and a generative model, which learns p(x). Every possible image competes for the same probability mass, so a generative model can reject unreasonable inputs. A taxonomy then splits generative models into those that can compute p(x) and those that can only sample. The lecture covers the two that compute (or approximate) it: autoregressive models factor p(x) with the chain rule into step-by-step predictions, and their weak point on raw pixels is speed; VAEs cannot compute p(x), so they maximize a lower bound, the ELBO, whose reconstruction and prior terms pull against each other. The schedule lists GANs under this lecture, but both the 2026 and 2025 slides place them at the start of the next one. This post covers them too so the three paradigms can be compared in one place.
Efficient Vision: What ViTs, GANs, Video, and Point Clouds Each Waste | MIT 6.5940 Lectures 16–17
Lecture 16 covers ViTs. At high resolution, attention cost grows with the square of the resolution. Window attention (Swin) confines computation to local windows, EfficientViT uses ReLU linear attention to get linear cost and then restores local and multi-scale ability, and SparseViT prunes unimportant windows. Self-supervised learning (contrastive learning, CLIP, MAE) answers the ViT's hunger for labeled data. HART pairs discrete tokens with residual diffusion and reaches several times the throughput of diffusion models. Lecture 17 targets three kinds of redundancy: 2D spatial in GANs (GAN Compression, AnyCost GAN, DiffAugment), temporal in video (TSM, temporal modeling at zero FLOPs), and 3D sparsity in point clouds (PVCNN, SPVCNN, BEVFusion). The Fall 2026 schedule drops Lecture 17.
GANs — How Do Two Competing Networks End Up Drawing Pictures? | NCCU Yen-Lung Tsai Generative AI Lecture 3
The prompt "a cute girl" has countless correct pictures, so training it as a function only teaches the model the average of all of them. GANs sidestep this by training two networks: a generator G turns a random latent vector into an image, a discriminator D judges real versus fake, and the two compete. L03 walks from the 2014 paper through WGAN, Progressive GAN, StyleGAN's 512-dimensional latent and AdaIN, then Pix2Pix and CycleGAN. An appendix explains cross entropy and KL divergence as a 'surprise index'. Week 3 homework: run a GAN yourself, or explain CE and KL in your own words.
(Part 2): SimCLR and GANs — Representations Without Labels, Generation Without Likelihoods | Harvard CS181 Assignment 5 / Part 3
HW5 Problems 1–2 both turn learning into a classification task. SimCLR's NT-Xent loss asks the network to pick the other augmented view of the same image out of 2N−1 candidates; a GAN's discriminator classifies real versus fake. You derive the math behind each (the cross-entropy equivalence, the optimal discriminator and the JS divergence), then write both training loops on FashionMNIST and MNIST.
Generative Models Basics — Density/Energy Models, GANs, Autoregressive, Diffusion | MIT 6.7960 Lecture 14
Generative models learn the data distribution p(x). Density models model probability directly, energy models use an unnormalized potential + sampler, GANs let a discriminator force realistic samples, autoregressive predicts the next token step by step, and diffusion dodges tricky maximum-likelihood via 'add noise then learn to denoise'.
Adversarial Robustness and Generative Models: It's Not Nonlinearity, It's Linearity | Stanford CS230 Lecture 4
Researchers initially assumed neural networks are easy to fool because they're nonlinear. That was wrong — Goodfellow's 2014 paper argues the primary cause is their linear nature, and high dimensionality lets every tiny perturbation compound. The second half covers generative models: GANs' three pathologies, and why diffusion sidesteps two of them by adding noise and learning to remove it.