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

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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 https://github.com/fhkingma/bitswap.

Friso H. Kingma, Pieter Abbeel, Jonathan Ho• 2019

Related benchmarks

TaskDatasetResultRank
Density EstimationMNIST (test)
NLL (bits/dim)1.27
56
Lossless CompressionCIFAR10 (test)
Bits Per Dimension (BPD)3.78
30
Density EstimationFashion (test)
NLL (bits/dim)3.28
27
Lossless CompressionImageNet32 (test)
BPD4.23
20
Lossless CompressionCIFAR10
BPD3.82
20
Lossless CompressionSVHN (test)
BPD2.55
16
Lossless CompressionCelebA 32x32 (test)
BPD3.82
16
Image CompressionImageNet-32
BPD4.5
14
Image CompressionImageNet 32x32 (test)
BPD4.5
13
Image CompressionCIFAR10
BPD3.82
13
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