Variational Autoencoders: Mathematical Derivation of the ELBO, the Reparameterization Trick, and Mitigating Posterior Collapse
Variational Autoencoders: Mathematical Derivation of the ELBO, the Reparameterization Trick, and Mitigating Posterior Collapse Traditional autoencoders map high-dimensional data into deterministic latent vectors. While effective for dimensionality reduction and non-linear feature compression, deterministic autoencoders fail as generative models because their latent representations lack continuous probabilistic structure. Unregularized latent spaces contain wide regions of empty space and severe
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