Variational Autoencoder with Arbitrary ConditioningDownload PDF

Published: 21 Dec 2018, Last Modified: 21 Apr 2024ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.
Keywords: unsupervised learning, generative models, conditional variational autoencoder, variational autoencoder, missing features multiple imputation, inpainting
TL;DR: We propose an extension of conditional variational autoencoder that allows conditioning on an arbitrary subset of the features and sampling the remaining ones.
Code: [![github](/images/github_icon.svg) tigvarts/ucm]( + [![Papers with Code](/images/pwc_icon.svg) 2 community implementations](
Data: [CelebA](
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 3 code implementations](
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