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Posterior Collapse: When Latents Vanish into the Prior

Chapter 16 · Mathematics for AI and Machine Learning · Advanced · 1:45

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.

More from Chapter 16

Interactive1:46The Reparameterization Trick: Differentiable SamplingChapter 16Preview1:27Monte Carlo: Averages That Close In Like a FunnelChapter 16Preview1:42Mean Field: Independent Factors for a Correlated PosteriorChapter 16Preview1:36Expectation Maximization: Coordinate Ascent on the Lower BoundChapter 16Preview1:55Importance-Weighted BoundsChapter 16