mlsanityData to 14 Oct 2026

Label Efficient Learning of Transferable Representations across Domains and Tasks

arXiv 1712.00123 ↗First posted 30 Nov 2017stat.ML cs.CVcited by 49

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Abstract (arXiv)

We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2017.

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Citing papers per year: 2021: 18; 2022: 14; 2023: 10; 2024: 1; 2025: 4; 2026: 2.

49 papers.

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  1. 2021Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restorationcited by 50
  2. 2023A Survey on Few-Shot Class-Incremental Learningcited by 48
  3. 2025Model Adaptation: Unsupervised Domain Adaptation without Source Datacited by 34
  4. 2021ECACL: A Holistic Framework for Semi-Supervised Domain Adaptationcited by 31
  5. 2021Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environmentscited by 30
  6. 2021Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representationscited by 27
  7. 2021Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptationcited by 24
  8. 2022GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysiscited by 19
  9. 2022Unsupervised Structure-Texture Separation Network for Oracle Character Recognitioncited by 15
  10. 2021CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networkscited by 12
  11. 2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
  12. 2021Transfer Learning for Pose Estimation of Illustrated Characterscited by 10
37 more
  1. 2022From Big to Small: Adaptive Learning to Partial-Set Domainscited by 9
  2. 2023Differentially Private Video Activity Recognitioncited by 9
  3. 2021Task Affinity with Maximum Bipartite Matching in Few-Shot Learningcited by 8
  4. 2022Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modelingcited by 8
  5. 2021Fisher Task Distance and Its Application in Neural Architecture Searchcited by 7
  6. 2022Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labelscited by 7
  7. 2023Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillationcited by 7
  8. 2025Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentationcited by 7
  9. 2021MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samplescited by 6
  10. 2022How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentationcited by 6
  11. 2021Multi-task Learning by Leveraging the Semantic Informationcited by 5
  12. 2022When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Frameworkcited by 5
  13. 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
  14. 2023Oracle Character Recognition using Unsupervised Discriminative Consistency Networkcited by 5
  15. 2021Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decompositioncited by 4
  16. 2022Gap Minimization for Knowledge Sharing and Transfercited by 4
  17. 2022Metric Based Few-Shot Graph Classificationcited by 4
  18. 2023Interpretations of Domain Adaptations via Layer Variational Analysiscited by 4
  19. 2025FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representationscited by 4
  20. 2021Improved Automated Machine Learning from Transfer Learningcited by 3
  21. 2022Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecuritycited by 3
  22. 2023Bayesian Transfer Learningcited by 3
  23. 2025Leveraging Transfer Learning and Mobile-enabled Convolutional Neural Networks for Improved Arabic Handwritten Character Recognitioncited by 3
  24. 2021Learning Purified Feature Representations from Task-irrelevant Labelscited by 2
  25. 2023Mode-Aware Continual Learning for Conditional Generative Adversarial Networkscited by 2
  26. 2021Learning Invariant Representations across Domains and Taskscited by 1
  27. 2021More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentationcited by 1
  28. 2021Self-Adaptive Partial Domain Adaptationcited by 1
  29. 2022Cycle Label-Consistent Networks for Unsupervised Domain Adaptationcited by 1
  30. 2022Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrievalcited by 1
  31. 2023Efficient Large-Scale Visual Representation Learning And Evaluationcited by 1
  32. 2022A Survey on Computational Intelligence-based Transfer Learningcited by 0
  33. 2023Domain Adaptive Semantic Segmentation by Optimal Transportcited by 0
  34. 2023Incorporating Experts' Judgment into Machine Learning Modelscited by 0
  35. 2024Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognitioncited by 0
  36. 2026Generative Cross-Entropy: A Strictly Proper Loss for Data-Efficient Classificationcited by 0
  37. 2026A Transfer Learning Evaluation of Deep Neural Networks for Image Classificationcited by 0