In recent years, deep neural network (DNN) compression systems have proved to
be highly effective for designing source codes for many natural sources.
However, like many other machine learning systems, these compressors suffer
from vulnerabilities to distribution shifts as well as out-of-distribution
(OOD) data, which reduces their real-world applications. In this paper, we
initiate the study of OOD robust compression. Considering robustness to two
types of ambiguity sets (Wasserstein balls and group shifts), we propose
algorithmic and architectural frameworks built on two principled methods: one
that trains DNN compressors using distributionally-robust optimization (DRO),
and the other which uses a structured latent code. Our results demonstrate that
both methods enforce robustness compared to a standard DNN compressor, and that
using a structured code can be superior to the DRO compressor. We observe
tradeoffs between robustness and distortion and corroborate these findings
theoretically for a specific class of sources.

本文介绍了两种增强深度神经网络压缩系统鲁棒性的方法，采用分布鲁棒优化和结构化潜在代码进行训练，取得较传统深度神经网络压缩器更优的效果。