Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1905.06845 v4 pith:JDCBK4MW submitted 2019-05-16 cs.LG cs.AIcs.ITmath.ITstat.COstat.ML

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

classification cs.LG cs.AIcs.ITmath.ITstat.COstat.ML
keywords latentcompressionbits-backmodelsvariablelosslessbit-swaphierarchical
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original 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 https://github.com/fhkingma/bitswap.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression

    cs.CV 2026-08 conditional novelty 6.0

    A LiDAR codec that keeps the most significant range bits in a self-contained stream and encodes the rest in a FIFO stream, making any prefix of the truncatable stream decode to a deterministically coarser point cloud.