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.

翻译：我们利用潜伏变量模型开发一种简单而优雅的无损压缩方法,我们称之为“与非对称数字系统(BB-ANS)回溯的比特 ” (BB-ANS ) 。 这种方法涉及连接编码和解码步骤,并在压缩数据批量时达到最佳速率。 我们首先在MNIST测试集上展示这一方法, 表明使用一个小的变异自动编码模型( VAE) 模型, 最先进的无损压缩是可能的。 然后我们运用了一种新颖的经验洞察力, 即经过小图像培训的完全进化基因模型能够对任意大小的图像进行概括, 并将BB- ANS 扩展至等级的潜伏变量模型, 使图像网络数据集中全尺寸彩色图像的无损状态压缩成为可能。 我们描述了“ Craystack ” 模块软件框架, 这是一种我们开发的模块化软件框架, 用于利用深重基因模型快速进行压缩的原型。