In many real-world machine learning applications, samples belong to a set of
domains e.g., for product reviews each review belongs to a product category. In
this paper, we study multi-domain imbalanced learning (MIL), the scenario that
there is imbalance not only in classes but also in domains. In the MIL setting,
different domains exhibit different patterns and there is a varying degree of
similarity and divergence among domains posing opportunities and challenges for
transfer learning especially when faced with limited or insufficient training
data. We propose a novel domain-aware contrastive knowledge transfer method
called DCMI to (1) identify the shared domain knowledge to encourage positive
transfer among similar domains (in particular from head domains to tail
domains); (2) isolate the domain-specific knowledge to minimize the negative
transfer from dissimilar domains. We evaluated the performance of DCMI on three
different datasets showing significant improvements in different MIL scenarios.

本文研究了多领域不平衡学习（MIL）的情形，提出了一种名为 DCMI 的领域感知对比知识传递方法来鼓励正向传递相似领域间的共享领域知识并最小化来自不同领域的负向传递。在三个不同的数据集上评估了 DCMI 的性能，展示了不同的 MIL 方案中显著的改进。