mlsanityData to 7 Oct 2026

Importance Weighted Adversarial Nets for Partial Domain Adaptation

arXiv 1803.09210 ↗First posted 25 Mar 2018cs.CVcited by 90

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Abstract (arXiv)

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. This paper extends the adversarial nets-based domain adaptation and proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains.

Key facts. Cited by 90 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Mar 2018.

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Citing papers per year: 2021: 26; 2022: 23; 2023: 16; 2024: 15; 2025: 7; 2026: 3.

90 papers.

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  1. 2021Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Datacited by 152
  2. 2022Unified Optimal Transport Framework for Universal Domain Adaptationcited by 57
  3. 2021DINE: Domain Adaptation from Single and Multiple Black-box Predictorscited by 51
  4. 2021Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iteratecited by 43
  5. 2021INTERN: A New Learning Paradigm Towards General Visioncited by 33
  6. 2021Transformer-Based Source-Free Domain Adaptationcited by 30
  7. 2023ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learningcited by 28
  8. 2021Learning from Imperfect Demonstrations from Agents with Varying Dynamicscited by 26
  9. 2023Semantic Image Segmentation: Two Decades of Researchcited by 26
  10. 2022Source-Free Progressive Graph Learning for Open-Set Domain Adaptationcited by 25
  11. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  12. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 22
38 more
  1. 2022SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognitioncited by 21
  2. 2021Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Surveycited by 19
  3. 2021Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Networkcited by 17
  4. 2022Subsidiary Prototype Alignment for Universal Domain Adaptationcited by 17
  5. 2022MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptationcited by 15
  6. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  7. 2021Divergence Optimization for Noisy Universal Domain Adaptationcited by 13
  8. 2021Semi-supervised Models are Strong Unsupervised Domain Adaptation Learnerscited by 13
  9. 2021Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptationcited by 12
  10. 2022Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Surveycited by 12
  11. 2021Domain-robust VQA with diverse datasets and methods but no target labelscited by 11
  12. 2021Partial Domain Adaptation without Domain Alignmentcited by 11
  13. 2022From Big to Small: Adaptive Learning to Partial-Set Domainscited by 9
  14. 2023Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clusteringcited by 9
  15. 2023Activate and Reject: Towards Safe Domain Generalization under Category Shiftcited by 9
  16. 2021Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentationcited by 8
  17. 2022Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modelingcited by 8
  18. 2022Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptationcited by 8
  19. 2023DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detectioncited by 8
  20. 2023Context-aware Domain Adaptation for Time Series Anomaly Detectioncited by 8
  21. 2023Generalized Universal Domain Adaptation with Generative Flow Networkscited by 7
  22. 2024Prototypical Partial Optimal Transport for Universal Domain Adaptationcited by 7
  23. 2023Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptationcited by 6
  24. 2024Generalizable Face Landmarking Guided by Conditional Face Warpingcited by 6
  25. 2022Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptationcited by 5
  26. 2022Fast and Accurate Importance Weighting for Correcting Sample Biascited by 5
  27. 2021Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computationcited by 4
  28. 2021On Universal Black-Box Domain Adaptationcited by 4
  29. 2022Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptationcited by 4
  30. 2024Memory-Efficient Pseudo-Labeling for Online Source-Free Universal Domain Adaptation using a Gaussian Mixture Modelcited by 4
  31. 2021Universal Domain Adaptation in Ordinal Regressioncited by 3
  32. 2021Reciprocal Normalization for Domain Adaptationcited by 3
  33. 2022Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithmcited by 3
  34. 2022Cross-lingual Adaptation for Recipe Retrieval with Mixupcited by 3
  35. 2022Deep face recognition with clustering based domain adaptationcited by 3
  36. 2022Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptationcited by 3
  37. 2022Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptationcited by 2
  38. 2023A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignmentcited by 2

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