Posterior Collapse: When Latents Vanish into the Prior
An interactive live film: excessive regularization or overly powerful decoders force the approximate posterior to collapse into the standard normal prior, causing latents to lose all representation power.
Narration
Variational autoencoders optimize the evidence lower bound, balancing two opposing objectives: reconstruction fidelity and divergence from the prior distribution.
The reconstruction term encourages the encoder to retain maximum detail. Simultaneously, the relative entropy term pulls each posterior distribution toward the standard Gaussian prior.
The rest of the narration is in the book's Companion.
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Preview1:27Monte Carlo: Averages That Close In Like a FunnelChapter 16
Preview1:42Mean Field: Independent Factors for a Correlated PosteriorChapter 16
Preview1:36Expectation Maximization: Coordinate Ascent on the Lower BoundChapter 16
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