Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (2007.08801v3)

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}

• The paper introduces LtC-MSDA, a novel framework for multi-source domain adaptation that leverages a knowledge graph to integrate information from diverse source domains.
• It employs graph-based prediction alongside a Relation Alignment Loss to ensure global consistency and local feature compactness across domains.
• Empirical evaluations on benchmarks like Digits-five, Office-31, DomainNet, and PACS demonstrate significant performance gains over previous state-of-the-art methods.

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

The paper "Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation" introduces an innovative framework designed to address the challenges inherent in multi-source domain adaptation (MSDA). Unlike traditional single-source domain adaptation problems, MSDA requires the integration of knowledge from multiple source domains to enhance performance on an unlabeled target domain. The authors propose a novel approach, Learning to Combine for MSDA (LtC-MSDA), which emphasizes the interactions among multiple domains to facilitate improved domain adaptation.

Core Contributions

The LtC-MSDA framework introduces several key innovations aimed at refining how information is aggregated from multiple domains:

1. Knowledge Graph Construction: The authors utilize a knowledge graph constructed on the prototypes of various domains to facilitate information propagation. This graph allows semantically related representations from different domains to interact, thereby enhancing the capability of the model to handle diverse modal information.
2. Graph-based Prediction: Within this framework, a graph model is employed to predict query samples based on the constructed knowledge graph. This method allows for a learnable combination of domains, contrasting with previous approaches that rely on hand-crafted weighting schemes.
3. Relation Alignment Loss (RAL): This loss function is introduced to ensure the consistency of category relations across domains. It includes:
   • A global relation constraint that maintains relational interdependency among categories throughout all domains.
   • A local relation constraint to promote feature compactness, improving intra-class invariance and inter-class separability.

Performance and Evaluation

The effectiveness of the proposed method is demonstrated through comprehensive experiments conducted on several benchmark datasets, including Digits-five, Office-31, DomainNet, and PACS. Key findings from these experiments highlight:

• The LtC-MSDA framework consistently outperforms existing state-of-the-art approaches across all tested datasets. The method shows notable improvements, especially in datasets with significant domain shifts and complex category distributions, such as DomainNet and PACS.
• Results on Digits-five demonstrate the framework's effectiveness in dealing with diverse modalities, with a remarkable gain of 7% on particular tasks compared to previous methods.

Practical and Theoretical Implications

This research presents important theoretical implications for MSDA by introducing a structured method to leverage inter-domain relationships via knowledge graphs. The practical implications are equally important; the framework's ability to manage large and diverse datasets with varying levels of complexity speaks to its potential utility in real-world applications where data heterogeneity often poses significant challenges.

Future Directions

The successful implementation and results garnered by the LtC-MSDA framework open up avenues for further research, particularly:

• Expansion to Other Domains: Future work may explore the application of this framework in other domains outside of computer vision, where MSDA could enhance model transferability and robustness.
• Refinement of Loss Components: There is potential to further refine the global and local constraints within the RAL to enhance alignment and compactness further.
• Scalability: As data sizes grow, optimizing the computational efficiency of the knowledge graph and its integration into larger models could be a pertinent focus.

The paper "Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation" stands as a significant contribution to the field, offering a robust framework that not only addresses current challenges in MSDA but also sets a foundation for future innovations.