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

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.

Cite

Text

Kingma et al. "Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables." International Conference on Machine Learning, 2019.

Markdown

[Kingma et al. "Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/kingma2019icml-bitswap/)

BibTeX

@inproceedings{kingma2019icml-bitswap,
  title     = {{Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables}},
  author    = {Kingma, Friso and Abbeel, Pieter and Ho, Jonathan},
  booktitle = {International Conference on Machine Learning},
  year      = {2019},
  pages     = {3408-3417},
  volume    = {97},
  url       = {https://mlanthology.org/icml/2019/kingma2019icml-bitswap/}
}