Generalization Gap in Amortized Inference

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

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Authors

Mingtian Zhang, Peter Hayes, David Barber

Abstract

The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression. In this work, we study the generalization of a popular class of probabilistic model - the Variational Auto-Encoder (VAE). We discuss the two generalization gaps that affect VAEs and show that overfitting is usually dominated by amortized inference. Based on this observation, we propose a new training objective that improves the generalization of amortized inference. We demonstrate how our method can improve performance in the context of image modeling and lossless compression.