Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling

• URL: http://arxiv.org/abs/2308.09078v2
• Date: Wed, 8 Nov 2023 15:37:27 GMT
• Title: Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
• Authors: Vaidotas Simkus and Michael U. Gutmann
• Abstract summary: Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice forally exact conditional sampling is Metropolis-within-Gibbs (MWG)
• Score: 7.357511266926065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice for asymptotically exact conditional sampling is Metropolis-within-Gibbs (MWG). However, we observe that the tendency of VAEs to learn a structured latent space, a commonly desired property, can cause the MWG sampler to get "stuck" far from the target distribution. This paper mitigates the limitations of MWG: we systematically outline the pitfalls in the context of VAEs, propose two original methods that address these pitfalls, and demonstrate an improved performance of the proposed methods on a set of sampling tasks.

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