Stanford CS236: Deep Generative Models I 2023 I Lecture 8 - GANs

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deepgenerativemodels.github.io/
Stefano Ermon
Associate Professor of Computer Science, Stanford University
cs.stanford.edu/~ermon/
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17:55? Log-likelihood derivation
21:50 Parameterize f theta or f inverse theta
28:50 Desiderata, Jacobians of special structure for fast determinant computation, lower triangular matrix determinant is product of diagonal elements
33:00? Derivation of Jacobian (simple layered coupling)
36:55 Simple layered coupling model is already able to map a complex distribution over pixels to a Gaussian (volume preserving)
42:00? Derivation of Jacobian (layered coupling with scaled shifts)
45:30? Generation process is deterministic
47:20 Sample data to get z, interpolation of z generates interpolated samples. Proves that while z are not compressive, they are meaningful latent representations.
50:00? Flow interpretation of autoregressive gaussian model
56:00? Inverse mapping computed in parallel
56:30? Jacobian lower diagonal because x_i only depends on z_

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First