mlsanityData to 7 Oct 2026

LibFewShot: A Comprehensive Library for Few-shot Learning

arXiv 2109.04898 ↗First posted 10 Sep 2021cs.CVcited by 21

Not on the map. Why?

Abstract (arXiv)

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or ``tricks'', such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, with respect to the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results confirm that such a mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to enter the area of few-shot learning but also elucidate the effects of nontrivial tricks to facilitate intrinsic research on few-shot learning. The source code is available from https://github.com/RL-VIG/LibFewShot.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 47 of its 82 references that are in the map. First posted on arXiv 10 Sep 2021.

Builds on

47 of 82 references.

References · 47

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  8. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  9. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  10. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  11. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  12. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
35 more
  1. 2016Matching Networks for One Shot Learningcited by 2,246
  2. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  3. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  4. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  5. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  6. 2021CvT: Introducing Convolutions to Vision Transformerscited by 946
  7. 2018AutoAugment: Learning Augmentation Policies from Datacited by 728
  8. 2019A Closer Look at Few-shot Classificationcited by 679
  9. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  10. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  11. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  12. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  13. 2018Bag of Tricks for Image Classification with Convolutional Neural Networkscited by 430
  14. 2018Meta-learning with differentiable closed-form solverscited by 395
  15. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  16. 2019Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLcited by 359
  17. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  18. 2014Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networkscited by 299
  19. 2019A Baseline for Few-Shot Image Classificationcited by 298
  20. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 293
  21. 2018DropBlock: A regularization method for convolutional networkscited by 261
  22. 2019Cross Attention Network for Few-shot Classificationcited by 218
  23. 2020Bag of Tricks for Adversarial Trainingcited by 199
  24. 2020CrossTransformers: spatially-aware few-shot transfercited by 174
  25. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  26. 2019Boosting Few-Shot Visual Learning with Self-Supervisioncited by 140
  27. 2019Charting the Right Manifold: Manifold Mixup for Few-shot Learningcited by 135
  28. 2020Negative Margin Matters: Understanding Margin in Few-shot Classificationcited by 129
  29. 2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
  30. 2020Few-Shot Classification with Feature Map Reconstruction Networkscited by 116
  31. 2019When Does Self-supervision Improve Few-shot Learning?cited by 78
  32. 2021Relational Embedding for Few-Shot Classificationcited by 74
  33. 2020Self-supervised Knowledge Distillation for Few-shot Learningcited by 41
  34. 2019Meta-Transfer Learning through Hard Taskscited by 23
  35. 2020Asymmetric Distribution Measure for Few-shot Learningcited by 18

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 7; 2025: 5; 2026: 1.

21 papers.

Sort:

By area:

  1. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  2. 2023Does Few-shot Learning Suffer from Backdoor Attacks?cited by 19
  3. 2025Reliable Few-shot Learning under Dual Noisescited by 16
  4. 2022Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learningcited by 6
  5. 2024Benchmarking Spurious Bias in Few-Shot Image Classifierscited by 6
  6. 2024MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlationcited by 5
  7. 2024Towards Few-Shot Learning in the Open World: A Review and Beyondcited by 5
  8. 2022Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learningcited by 4
  9. 2024Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Predictioncited by 2
  10. 2026SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classificationcited by 2
  11. 2023TablEye: Seeing small Tables through the Lens of Imagescited by 1
  12. 2023Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditionscited by 1
9 more
  1. 2024Multi-Level Correlation Network For Few-Shot Image Classificationcited by 1
  2. 2023Gestalt-Guided Image Understanding for Few-Shot Learningcited by 0
  3. 2023FewSAR: A Few-shot SAR Image Classification Benchmarkcited by 0
  4. 2024Reading ability detection using eye-tracking data with LSTM-based few-shot learningcited by 0
  5. 2024LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learningcited by 0
  6. 2025Few-shot Novel Category Discoverycited by 0
  7. 2025Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoringcited by 0
  8. 2025Unsupervised Active Learning via Natural Feature Progressive Frameworkcited by 0
  9. 2025Provably Robust Adaptation for Language-Empowered Foundation Modelscited by 0