mlsanityData to 7 Oct 2026

Cross-domain few-shot learning with unlabelled data

arXiv 2101.07899 ↗First posted 19 Jan 2021cs.CV cs.LGcited by 10

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Abstract (arXiv)

Few shot learning aims to solve the data scarcity problem. If there is a domain shift between the test set and the training set, their performance will decrease a lot. This setting is called Cross-domain few-shot learning. However, this is very challenging because the target domain is unseen during training. Thus we propose a new setting some unlabelled data from the target domain is provided, which can bridge the gap between the source domain and the target domain. A benchmark for this setting is constructed using DomainNet citepeng2018oment. We come up with a self-supervised learning method to fully utilize the knowledge in the labeled training set and the unlabelled set. Extensive experiments show that our methods outperforms several baseline methods by a large margin. We also carefully design an episodic training pipeline which yields a significant performance boost.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 19 Jan 2021.

Builds on

17 of 28 references.

References · 17

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  5. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  6. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  7. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  8. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  9. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
  10. 2019A Closer Look at Few-shot Classificationcited by 679
  11. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  12. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
5 more
  1. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 216
  2. 2020Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-IDcited by 212
  3. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 183
  4. 2020Feature Alignment and Restoration for Domain Generalization and Adaptationcited by 30
  5. 2018Disjoint Label Space Transfer Learning with Common Factorised Spacecited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

10 papers.

  1. 2022TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learningcited by 16
  2. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  3. 2022Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognitioncited by 4
  4. 2023Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classificationcited by 4
  5. 2021Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifierscited by 3
  6. 2023A Survey of Deep Visual Cross-Domain Few-Shot Learningcited by 3
  7. 2024DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognitioncited by 3
  8. 2025A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mappingcited by 0
  9. 2025FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learningcited by 0
  10. 2026FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentationcited by 0