Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal to the KL divergence between the approximate posterior and the true posterior. In this paper, we show how to remove this gap asymptotically by deriving bits-back coding algorithms from tighter variational bounds. The key idea is to exploit extended space representations of Monte Carlo estimators of the marginal likelihood. Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space. When parallel architectures can be exploited, our coders can achieve better rates than bits-back with little additional cost. We demonstrate improved lossless compression rates in a variety of settings, including entropy coding for lossy compression.

本文介绍了利用更紧密的变分上限构建的bits-back编码算法来消除KL散度对速率效率的影响，并提出了利用延伸空间表示来优化MC估计的关键思想，并通过在潜在空间中耦合来大大减少额外成本，最终实现了较bits-back更好的无损压缩速率。

通过蒙特卡洛比特回传编码提高无损压缩速率