Stanford CS236: Deep Generative Models I 2023 I Lecture 9 - Normalizing Flows

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Stefano Ermon
Associate Professor of Computer Science, Stanford University
cs.stanford.edu/~ermon/
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[@user-zr4ns3hu6y]

I think the titles of lecture 8 and lecture 9 have been switched.

[@CPTSMONSTER]

15:15 High likelihood and bad samples, garbage component is a constant in log-likelihood 40:00? Expectation on p data and p theta, how was this chosen 46:35 Note optimization of phi (discriminator) and theta (generator of fake samples) 50:45 Likelihood model in discriminator, but GANs can avoid likelihoods 1:00:15? Expectation on p data and p theta, added? 1:06:50 Minimax training objective 1:15:00 GANs no longer state of the art, very hard to train, mode collapse, no clean loss function to evaluate