Zhang, M;
Hayes, P;
Barber, D;
(2022)
Generalization Gap in Amortized Inference.
In:
Advances in Neural Information Processing Systems.
NIPS
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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.
Type: | Proceedings paper |
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Title: | Generalization Gap in Amortized Inference |
Event: | 36th Conference on Neural Information Processing Systems (NeurIPS 2022) |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://papers.nips.cc/ |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10173889 |
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