Generalization Gap in Amortized Inference

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.

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Cite

Text

Zhang et al. "Generalization Gap in Amortized Inference." Neural Information Processing Systems, 2022.

Markdown

[Zhang et al. "Generalization Gap in Amortized Inference." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/zhang2022neurips-generalization/)

BibTeX

@inproceedings{zhang2022neurips-generalization,
  title     = {{Generalization Gap in Amortized Inference}},
  author    = {Zhang, Mingtian and Hayes, Peter and Barber, David},
  booktitle = {Neural Information Processing Systems},
  year      = {2022},
  url       = {https://mlanthology.org/neurips/2022/zhang2022neurips-generalization/}
}