We consider lossy compression of an information source when decoder-only side information may be absent. This setup, also referred to as the Heegard-Berger or Kaspi problem, is a special case of robust distributed source coding. Building upon previous works on neural network-based distributed compressors developed for the decoder-only side information (Wyner-Ziv) case, we propose learning-based schemes that are amenable to the availability of side information. We find that our learned compressors mimic the achievability part of the Heegard-Berger theorem and yield interpretable results operating close to information-theoretic bounds. Depending on the availability of the side information, our neural compressors recover characteristics of the point-to-point (i.e., with no side information) and the Wyner-Ziv coding strategies that include binning in the source space, although no structure exploiting knowledge of the source and side information was imposed into the design.

翻译：我们考虑当解码器端边信息可能缺失时信息源的有损压缩问题。该设置（亦称Heegard-Berger或Kaspi问题）是鲁棒分布式信源编码的特例。基于先前为仅解码器端边信息（Wyner-Ziv）场景开发的神经网络分布式压缩器研究工作，我们提出了适应边信息可用性的学习驱动方案。研究发现，我们的学习型压缩器能够复现Heegard-Berger定理的可达性部分，并产生接近信息论界限的可解释结果。根据边信息可用性的不同，我们的神经压缩器恢复了点对点（即无边信息）和Wyner-Ziv编码策略的特征——这包括源空间装箱操作，尽管设计中并未施加利用源与边信息先验知识的显式结构约束。