mlsanityData to 14 Oct 2026

Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation

arXiv 2302.02561 ↗First posted 6 Feb 2023cs.LG cs.AIcited by 20

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Abstract (arXiv)

Previous studies have shown that leveraging domain index can significantly boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628). However, such domain indices are not always available. To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance. Our theoretical analysis shows that our adversarial variational Bayesian framework finds the optimal domain index at equilibrium. Empirical results on both synthetic and real data verify that our model can produce interpretable domain indices which enable us to achieve superior performance compared to state-of-the-art domain adaptation methods. Code is available at https://github.com/Wang-ML-Lab/VDI.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

32 of 47 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  4. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  5. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,331
  6. 2017Adversarial Discriminative Domain Adaptationcited by 1,311
  7. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,200
  8. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 683
  9. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 600
  10. 2017Learning multiple visual domains with residual adapterscited by 557
  11. 2019Bridging Theory and Algorithm for Domain Adaptationcited by 332
  12. 2017Generate To Adapt: Aligning Domains using Generative Adversarial Networkscited by 183
20 more
  1. 2019Domain Generalization Using a Mixture of Multiple Latent Domainscited by 170
  2. 2020Understanding Self-Training for Gradual Domain Adaptationcited by 149
  3. 2020Neural Unsupervised Domain Adaptation in NLP---A Surveycited by 144
  4. 2015A Large-Scale Car Dataset for Fine-Grained Categorization and Verificationcited by 130
  5. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 121
  6. 2014Collaborative Deep Learning for Recommender Systemscited by 95
  7. 2020SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptationcited by 78
  8. 2017AutoDIAL: Automatic DomaIn Alignment Layerscited by 75
  9. 2020Continuously Indexed Domain Adaptationcited by 75
  10. 2016Towards Bayesian Deep Learning: A Framework and Some Existing Methodscited by 58
  11. 2020Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillationcited by 47
  12. 2019Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networkscited by 37
  13. 2019DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patternscited by 33
  14. 2019AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphscited by 32
  15. 2021A Bit More Bayesian: Domain-Invariant Learning with Uncertaintycited by 27
  16. 2022Gradual Domain Adaptation without Indexed Intermediate Domainscited by 26
  17. 2020Domain2Vec: Domain Embedding for Unsupervised Domain Adaptationcited by 24
  18. 2020StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardizationcited by 23
  19. 2022Graph-Relational Domain Adaptationcited by 19
  20. 2019Adaptation Across Extreme Variations using Unlabeled Domain Bridgescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 6; 2026: 3.

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  1. 2023A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithmcited by 33
  2. 2024Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretationscited by 33
  3. 2023DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalizationcited by 24
  4. 2024Continuous Temporal Domain Generalizationcited by 15
  5. 2023Taxonomy-Structured Domain Adaptationcited by 6
  6. 2023Continuous Invariance Learningcited by 6
  7. 2024Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Modelscited by 6
  8. 2025Concept-Based Unsupervised Domain Adaptationcited by 4
  9. 2023Pre-trained Recommender Systems: A Causal Debiasing Perspectivecited by 2
  10. 2025PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generationcited by 2
  11. 2025Continuous Domain Generalizationcited by 2
  12. 2024Towards Domain Adaptive Neural Contextual Banditscited by 1
8 more
  1. 2024Variational Language Concepts for Interpreting Foundation Language Modelscited by 1
  2. 2025Beyond Batch Learning: Global Awareness Enhanced Domain Adaptationcited by 1
  3. 2025Noise Optimized Conditional Diffusion for Domain Adaptationcited by 1
  4. 2024Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexingcited by 0
  5. 2025Global Variational Inference Enhanced Robust Domain Adaptationcited by 0
  6. 2026Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Modelscited by 0
  7. 2026Probabilistic Residual Learning for Online Recommendationscited by 0
  8. 2026When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoningcited by 0