mlsanityData to 7 Oct 2026

Adversarial Feature Augmentation for Cross-domain Few-shot Classification

arXiv 2208.11021 ↗First posted 23 Aug 2022cs.CVcited by 36

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Abstract (arXiv)

Existing methods based on meta-learning predict novel-class labels for (target domain) testing tasks via meta knowledge learned from (source domain) training tasks of base classes. However, most existing works may fail to generalize to novel classes due to the probably large domain discrepancy across domains. To address this issue, we propose a novel adversarial feature augmentation (AFA) method to bridge the domain gap in few-shot learning. The feature augmentation is designed to simulate distribution variations by maximizing the domain discrepancy. During adversarial training, the domain discriminator is learned by distinguishing the augmented features (unseen domain) from the original ones (seen domain), while the domain discrepancy is minimized to obtain the optimal feature encoder. The proposed method is a plug-and-play module that can be easily integrated into existing few-shot learning methods based on meta-learning. Extensive experiments on nine datasets demonstrate the superiority of our method for cross-domain few-shot classification compared with the state of the art. Code is available at https://github.com/youthhoo/AFA_For_Few_shot_learning

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

39 of 50 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  7. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  8. 2016Matching Networks for One Shot Learningcited by 2,246
  9. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  10. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  11. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  12. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
27 more
  1. 2019A Closer Look at Few-shot Classificationcited by 679
  2. 2019Adversarial Training for Free!cited by 638
  3. 2018Generalizing to Unseen Domains via Adversarial Data Augmentationcited by 451
  4. 2019Selective Kernel Networkscited by 401
  5. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
  6. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 333
  7. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  8. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 208
  9. 2017Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learningcited by 192
  10. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 183
  11. 2016Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptationcited by 162
  12. 2019Progressive Domain Adaptation for Object Detectioncited by 134
  13. 2019Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processescited by 131
  14. 2019Feature-Critic Networks for Heterogeneous Domain Generalizationcited by 130
  15. 2020Negative Margin Matters: Understanding Margin in Few-shot Classificationcited by 129
  16. 2019Improved Few-Shot Visual Classificationcited by 114
  17. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  18. 2021Cross-Domain Few-Shot Classification via Adversarial Task Augmentationcited by 63
  19. 2020Explanation-Guided Training for Cross-Domain Few-Shot Classificationcited by 58
  20. 2020TaskNorm: Rethinking Batch Normalization for Meta-Learningcited by 50
  21. 2020MVFNet: Multi-View Fusion Network for Efficient Video Recognitioncited by 36
  22. 2020Enhancing Few-Shot Image Classification with Unlabelled Examplescited by 32
  23. 2018StNet: Local and Global Spatial-Temporal Modeling for Action Recognitioncited by 28
  24. 2020Few-Shot One-Class Classification via Meta-Learningcited by 17
  25. 2021Memory Efficient Meta-Learning with Large Imagescited by 14
  26. 2020Knowledge Guided Metric Learning for Few-Shot Text Classificationcited by 8
  27. 2020Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 10; 2025: 9; 2026: 8.

36 papers.

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  1. 2023StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learningcited by 53
  2. 2024Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learningcited by 26
  3. 2023ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusioncited by 15
  4. 2023Simple Semantic-Aided Few-Shot Learningcited by 15
  5. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  6. 2024Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognitioncited by 14
  7. 2024Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learningcited by 13
  8. 2024Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learningcited by 11
  9. 2024Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Promptingcited by 9
  10. 2025VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learningcited by 7
  11. 2023Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillationcited by 6
  12. 2024Towards Few-Shot Learning in the Open World: A Review and Beyondcited by 5
24 more
  1. 2026Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learningcited by 5
  2. 2025Random Registers for Cross-Domain Few-Shot Learningcited by 4
  3. 2026Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learningcited by 4
  4. 2026Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignmentcited by 4
  5. 2023A Survey of Deep Visual Cross-Domain Few-Shot Learningcited by 3
  6. 2025Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learningcited by 3
  7. 2023Uncertainty-Aware Gradient Stabilization for Small Object Detectioncited by 2
  8. 2025Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservationcited by 2
  9. 2025Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learningcited by 2
  10. 2026DVLA-RL: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot Learningcited by 2
  11. 2026Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learningcited by 2
  12. 2023Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learningcited by 1
  13. 2024Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applicationscited by 1
  14. 2026Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learningcited by 1
  15. 2023Plug-and-Play Feature Generation for Few-Shot Medical Image Classificationcited by 0
  16. 2024Memory-guided Network with Uncertainty-based Feature Augmentation for Few-shot Semantic Segmentationcited by 0
  17. 2024Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rulescited by 0
  18. 2024Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learningcited by 0
  19. 2025A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mappingcited by 0
  20. 2025MetaLab: Few-Shot Game Changer for Image Recognitioncited by 0
  21. 2025Color as the Impetus: Transforming Few-Shot Learnercited by 0
  22. 2025FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learningcited by 0
  23. 2026FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentationcited by 0
  24. 2026PMCE: Probabilistic Multi-Granularity Semantics with Caption-Guided Enhancement for Few-Shot Learningcited by 0