  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.
