Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
This addresses multi-source domain adaptation for machine learning applications, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of transferring knowledge from multiple source domains to a target domain, which is more challenging than single-source adaptation, and proposes a framework that uses a knowledge graph on domain prototypes and a relation alignment loss to achieve state-of-the-art performance on benchmark datasets.
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}