Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation

• URL: http://arxiv.org/abs/2007.08801v3
• Date: Tue, 28 Jul 2020 15:12:38 GMT
• Title: Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
• Authors: Hang Wang, Minghao Xu, Bingbing Ni, Wenjun Zhang
• Abstract summary: We propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. Our approach outperforms existing methods with a remarkable margin.
• Score: 56.694330303488435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermore, the increase of modalities brings more difficulty in aligning feature distributions among multiple domains. To mitigate these problems, we propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework via exploring interactions among domains. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. On such basis, a graph model is learned to predict query samples under the guidance of correlated prototypes. In addition, we design a Relation Alignment Loss (RAL) to facilitate the consistency of categories' relational interdependency and the compactness of features, which boosts features' intra-class invariance and inter-class separability. Comprehensive results on public benchmark datasets demonstrate that our approach outperforms existing methods with a remarkable margin. Our code is available at \url{https://github.com/ChrisAllenMing/LtC-MSDA}

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