Uncertainty in latent representations of variational autoencoders optimized for visual tasks

• URL: http://arxiv.org/abs/2404.15390v2
• Date: Thu, 23 Jan 2025 19:13:33 GMT
• Title: Uncertainty in latent representations of variational autoencoders optimized for visual tasks
• Authors: Josefina Catoni, Domonkos Martos, Ferenc Csikor, Enzo Ferrante, Diego H. Milone, Balázs Meszéna, Gergő Orbán, Rodrigo Echeveste,
• Abstract summary: We investigate the properties of inference in Variational Autoencoders (VAEs)<n>We draw inspiration from classical computer vision to introduce an inductive bias into the VAE.<n>We find that restored inference capabilities are delivered by developing a motif in the inference network.
• Score: 3.9504737666460037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Generative Models (DGMs) can learn flexible latent variable representations of images while avoiding intractable computations, common in Bayesian inference. However, investigating the properties of inference in Variational Autoencoders (VAEs), a major class of DGMs, reveals severe problems in their uncertainty representations. Here we draw inspiration from classical computer vision to introduce an inductive bias into the VAE by incorporating a global explaining-away latent variable, which remedies defective inference in VAEs. Unlike standard VAEs, the Explaing-Away VAE (EA-VAE) provides uncertainty estimates that align with normative requirements across a wide spectrum of perceptual tasks, including image corruption, interpolation, and out-of-distribution detection. We find that restored inference capabilities are delivered by developing a motif in the inference network (the encoder) which is widespread in biological neural networks: divisive normalization. Our results establish EA-VAEs as reliable tools to perform inference under deep generative models with appropriate estimates of uncertainty.

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