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

Friso Kingma · Pieter Abbeel · Jonathan Ho

Pacific Ballroom #6

Keywords: [ Unsupervised Learning ] [ Information Theory and Estimation ] [ Generative Models ] [ Deep Generative Models ]


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.

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