mlsanityData to 7 Oct 2026

Secure Domain Adaptation with Multiple Sources

arXiv 2106.12124 ↗First posted 23 Jun 2021cs.LGcited by 12

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Abstract (arXiv)

Multi-source unsupervised domain adaptation (MUDA) is a framework to address the challenge of annotated data scarcity in a target domain via transferring knowledge from multiple annotated source domains. When the source domains are distributed, data privacy and security can become significant concerns and protocols may limit data sharing, yet existing MUDA methods overlook these constraints. We develop an algorithm to address MUDA when source domain data cannot be shared with the target or across the source domains. Our method is based on aligning the distributions of source and target domains indirectly via estimating the source feature embeddings and predicting over a confidence based combination of domain specific model predictions. We provide theoretical analysis to support our approach and conduct empirical experiments to demonstrate that our algorithm is effective.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 51 of its 83 references that are in the map. First posted on arXiv 23 Jun 2021.

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51 of 83 references.

References · 51

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38 more
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50 most cited of 51 All 51 (JSON)

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Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 5; 2025: 1.

12 papers.

  1. 2023Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioningcited by 14
  2. 2022Unsupervised Model Adaptation for Source-free Segmentation of Medical Imagescited by 13
  3. 2023Preserving Fairness in AI under Domain Shiftcited by 3
  4. 2023Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Datacited by 2
  5. 2023Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learningcited by 2
  6. 2025pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixupcited by 2
  7. 2023Robust Internal Representations for Domain Generalizationcited by 0
  8. 2024Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representationscited by 0
  9. 2024Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptationcited by 0
  10. 2024Unsupervised Federated Domain Adaptation for Segmentation of MRI Imagescited by 0
  11. 2024Unsupervised Domain Adaptation Using Compact Internal Representationscited by 0
  12. 2024Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloudcited by 0