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

ECCV 2020  ·  Hang Wang, Minghao Xu, Bingbing Ni, Wenjun Zhang ·

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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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Multi-Source Unsupervised Domain Adaptation Digits-five LtC-MSDA Accuracy 91.8 # 3
Multi-Source Unsupervised Domain Adaptation Office-31 LtC-MSDA Accuracy 84.6 # 2

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