Lossless Compression with Latent Variable Models

TL;DR

This paper introduces BB-ANS, a lossless compression method using latent variable models, demonstrating state-of-the-art results on MNIST and ImageNet with a new hierarchical approach and a modular software framework.

Contribution

It presents BB-ANS, a novel lossless compression technique leveraging latent variable models, including hierarchical VAEs, and introduces Craystack for rapid prototyping.

Findings

State-of-the-art lossless compression on MNIST using small VAEs.

Hierarchical models enable lossless compression of full-size ImageNet images.

The method achieves optimal compression rates for batch data.

Abstract

We develop a simple and elegant method for lossless compression using latent variable models, which we call 'bits back with asymmetric numeral systems' (BB-ANS). The method involves interleaving encode and decode steps, and achieves an optimal rate when compressing batches of data. We demonstrate it firstly on the MNIST test set, showing that state-of-the-art lossless compression is possible using a small variational autoencoder (VAE) model. We then make use of a novel empirical insight, that fully convolutional generative models, trained on small images, are able to generalize to images of arbitrary size, and extend BB-ANS to hierarchical latent variable models, enabling state-of-the-art lossless compression of full-size colour images from the ImageNet dataset. We describe 'Craystack', a modular software framework which we have developed for rapid prototyping of compression using deep generative models.