Stanford CS236: Deep Generative Models I 2023 I Lecture 10 - GANs

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Stefano Ermon
Associate Professor of Computer Science, Stanford University
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22:15? Reverse KL
35:40 Instead of optimizing over all possible functions T, optimize over an arbitrary set of neural network architectures. Similar flavor to importance sampling in VAEs.
36:45? Essentially the same as minimax training objective of GANs, p and q are expectations, T is the discriminator and optimization does not depend on likelihoods (p and q)
40:00? Supporting hyperplanes, convex hull, tangent?
41:35 KL divergences vs f divergences, doesn't depend on likelihoods, doesn't measure compression of data, flexible loss functions
48:40 Summary slide on divergences and training objectives
51:00? Example
1:04:00? Lipschitz constant
1:08:00? Earth mover distance, how is this different to the minimax training objective of GAN
1:09:30 Unlike f divergence, Earth mover distance doesn't give bounds
1:22:20 BiGAN, encoder is similar to VAE except deterministic, not trained by minimizing KL divergences like in the ELBO, trained by minimizing two sample test optimized by the discriminator