mlsanityData to 7 Oct 2026

Domain Aggregation Networks for Multi-Source Domain Adaptation

arXiv 1909.05352 ↗First posted 11 Sep 2019cs.LG stat.MLcited by 37

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Abstract (arXiv)

In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing characters of a new font using a set of different fonts. While most recent research has considered ad-hoc combination rules to address this problem, we extend previous work on domain discrepancy minimization to develop a finite-sample generalization bound, and accordingly propose a theoretically justified optimization procedure. The algorithm we develop, Domain AggRegation Network (DARN), is able to effectively adjust the weight of each source domain during training to ensure relevant domains are given more importance for adaptation. We evaluate the proposed method on real-world sentiment analysis and digit recognition datasets and show that DARN can significantly outperform the state-of-the-art alternatives.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2019.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 8; 2024: 9; 2025: 1; 2026: 3.

37 papers.

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  1. 2022Balancing Discriminability and Transferability for Source-Free Domain Adaptationcited by 58
  2. 2022Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Expertscited by 50
  3. 2023Subspace Identification for Multi-Source Domain Adaptationcited by 36
  4. 2022Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domaincited by 27
  5. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  6. 2023MADG: Margin-based Adversarial Learning for Domain Generalizationcited by 22
  7. 2021Information-theoretic regularization for Multi-source Domain Adaptationcited by 15
  8. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  9. 2022Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detectioncited by 14
  10. 2021Secure Domain Adaptation with Multiple Sourcescited by 12
  11. 2021Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularizationcited by 12
  12. 2024More is Better: Deep Domain Adaptation with Multiple Sourcescited by 9
25 more
  1. 2022Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptationcited by 7
  2. 2023Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptationcited by 7
  3. 2023Best-Effort Adaptationcited by 7
  4. 2022Self-Supervised Graph Neural Network for Multi-Source Domain Adaptationcited by 5
  5. 2022A Unified Meta-Learning Framework for Dynamic Transfer Learningcited by 5
  6. 2021Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layerscited by 4
  7. 2022Algorithms and Theory for Supervised Gradual Domain Adaptationcited by 4
  8. 2024Optimizing importance weighting in the presence of sub-population shiftscited by 3
  9. 2022Maximizing Conditional Independence for Unsupervised Domain Adaptationcited by 2
  10. 2023Multi-source domain adaptation for regressioncited by 2
  11. 2024AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regressioncited by 2
  12. 2021Learning to Transfer with von Neumann Conditional Divergencecited by 1
  13. 2023Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Modelscited by 1
  14. 2025Time Series Domain Adaptation via Latent Invariant Causal Mechanismcited by 1
  15. 2022Joint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptationcited by 0
  16. 2023Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptationcited by 0
  17. 2023A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classificationcited by 0
  18. 2024Unsupervised Federated Domain Adaptation for Segmentation of MRI Imagescited by 0
  19. 2024AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptationcited by 0
  20. 2024A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognitioncited by 0
  21. 2024Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptationcited by 0
  22. 2024Multi-Source Unsupervised Domain Adaptation with Prototype Aggregationcited by 0
  23. 2026Seeking Universal Shot Language Understanding Solutionscited by 0
  24. 2026Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controlscited by 0
  25. 2026Language-Induced Priors for Domain Adaptationcited by 0