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Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

2019·26 Zitationen·International Conference on Machine Learning
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26

Zitationen

3

Autoren

2019

Jahr

Abstract

The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem. Bits-Back with Asymmetric Numeral Systems (BB-ANS), recently proposed by Townsend et al. (2019), makes bits-back coding practically feasible for latent variable models with one latent layer, but it is inefficient for hierarchical latent variable models. In this paper we propose Bit-Swap, a new compression scheme that generalizes BB-ANS and achieves strictly better compression rates for hierarchical latent variable models with Markov chain structure. Through experiments we verify that Bit-Swap results in lossless compression rates that are empirically superior to existing techniques. Our implementation is available at this https URL.

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Autoren

Institutionen

Themen

Gaussian Processes and Bayesian InferenceGenerative Adversarial Networks and Image SynthesisMachine Learning in Healthcare
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