论文标题
多域失衡数据的域感染知识转移
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data
论文作者
论文摘要
在许多现实世界的机器学习应用程序中，样本属于一组域，例如，对于产品评论，每个评论属于产品类别。在本文中，我们研究了多域不平衡学习（MIL），即不仅在课程中而且在域中也存在不平衡的情况。在MIL环境中，不同的领域表现出不同的模式，并且在转移学习的机会和挑战之间存在不同程度的相似性和分歧，尤其是在面对有限或不足的培训数据时。我们提出了一种新颖的域感染知识转移方法，称为DCMI到（1）确定共享领域知识，以鼓励在相似领域（尤其是从头部域到尾部域）之间的积极转移； （2）隔离域特异性知识，以最大程度地减少不同域的负转移。我们在三个不同的数据集上评估了DCMI的性能，显示了不同的MIL场景的显着改善。
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.
