Stanford CS236: Deep Generative Models I 2023 I Lecture 15 - Evaluation of Generative Models

For more information about Stanford's Artificial Intelligence programs visit: stanford.io/ai
To follow along with the course, visit the course website:
deepgenerativemodels.github.io/
Stefano Ermon
Associate Professor of Computer Science, Stanford University
cs.stanford.edu/~ermon/
Learn more about the online course and how to enroll: online.stanford.edu/courses/c...
To view all online courses and programs offered by Stanford, visit: online.stanford.edu/

[@CPTSMONSTER]

10:30? Shannon code interpretation
11:25 Training a generative model based on maximum likelihood is equivalent to optimizing compression, comparison of models based on likelihoods is equivalent to comparing compression
22:30 GAN generates samples but getting likelihoods is difficult, kernel density approximation
33:40? VAE, compute likelihoods by annealed importance sampling
43:25? Sharpness, entropy of classifier, low entropy means classifier puts probability mass on one y leading to high sharpness. Diversity, entropy of marginal distribution of classifier.
45:50 c(y) is the marginal distribution over predicted labels when fitting synthetic samples, one sample would yield low entropy (unhappy), ideally c(y) uniform over possible y, sharpness and diversity are competing
54:00 Notation, x and x' are not global variable names
55:20? Kernel Hilbert space mapping, comparing features equivalent to comparing moments
1:05:20? VAEs have reconstruction loss embedded in them, GANs don't
1:07:55 Impossible to measure disentanglement in unlabeled data