Recent work by Marino et al. (2020) showed improved performance in sequential
density estimation by combining masked autoregressive flows with hierarchical
latent variable models. We draw a connection between such autoregressive
generative models and the task of lossy video compression. Specifically, we
view recent neural video compression methods (Lu et al., 2019; Yang et al.,
2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal
autoregressive transform, and propose avenues for enhancement based on this
insight. Comprehensive evaluations on large-scale video data show improved
rate-distortion performance over both state-of-the-art neural and conventional
video compression methods.

本文通过将自回归生成模型与有损视频压缩任务联系起来，提出了一种基于机器学习的视频压缩方法。大规模视频数据的全面评估表明，在视频数据的压缩比率及失真质量上，该方法优于传统的基于机器学习和基于传统技术的方法。

神经网络视频压缩的分层自回归建模

Hierarchical Autoregressive Modeling for Neural Video Compression

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
this https URL

本研究提出了一种新的压缩方案 Bit-Swap，用于实现具有 Markov 链结构的分层隐变量模型的失真压缩，实验表明此方案比现有技术更加优越。

Bit-Swap: 递归位回推编码用于层次化潜在变量无损压缩

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

We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The architecture shows close-to-optimal results on temporal modeling
of video data, competitive results on music modeling, and improved perceptual
grouping based on higher order abstractions, such as stochastic textures and
motion cues. We present results for fully supervised, semi-supervised, and
unsupervised tasks. The results suggest that the proposed architecture and
principles are powerful tools for learning a hierarchy of abstractions,
learning iterative inference and handling temporal information.

本文提出了一种基于递归的 Ladder 神经网络扩展，以此解决层次潜在变量模型中需要的推理问题，并证明该架构能够处理包括迭代推理和时间建模在内的各种复杂学习任务，涵盖视频数据的时间建模和提高基于高阶抽象的感知聚类等问题。作者在完全监督、半监督和非监督任务方面都取得了不错的结果，这表明所提出的架构和原则是学习抽象层次结构、学习迭代推理和处理时间信息的强有力工具。