The Tilted Variational Autoencoder: Improving Out-of-Distribution DetectionDownload PDF

Published: 01 Feb 2023, Last Modified: 02 Mar 2023ICLR 2023 posterReaders: Everyone
Keywords: Variational Autoencoder, OOD, Unsupervised
TL;DR: A generalization of the Gaussian distribution improves the performance of out-of-distribution detection with variational autoencoders.
Abstract: A problem with using the Gaussian distribution as a prior for the variational autoencoder (VAE) is that the set on which Gaussians have high probability density is small as the latent dimension increases. This is an issue because VAEs try to attain both a high likelihood with respect to a prior distribution and at the same time, separation between points for better reconstruction. Therefore, a small volume in the high-density region of the prior is problematic because it restricts the separation of latent points. To ameliorate this, we propose a simple generalization of the Gaussian distribution, called the tilted Gaussian, which has a maximum probability density occurring on a sphere instead of a single point. The tilted Gaussian has exponentially more volume in high-density regions than the standard Gaussian as a function of the distribution dimension. We empirically demonstrate that this simple change in the prior distribution improves VAE performance on the task of detecting unsupervised out-of-distribution (OOD) samples. We also introduce a new OOD testing procedure, called the Will-It-Move test, where the tilted Gaussian achieves remarkable OOD performance.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Probabilistic Methods (eg, variational inference, causal inference, Gaussian processes)
10 Replies