mlsanityData to 7 Oct 2026

Prototypical Networks for Few-shot Learning

arXiv 1703.05175 ↗First posted 15 Mar 2017cs.LG stat.MLcited by 3,001

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Cited by 55 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend prototypical networks to zero-shot learning and achieve state-of-the-art results on the CU-Birds dataset.

Key facts. Cited by 3,001 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2017.

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Citing papers per year: 2018: 4; 2019: 0; 2020: 1; 2021: 642; 2022: 629; 2023: 567; 2024: 476; 2025: 377; 2026: 305.

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  1. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  2. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  3. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  4. 2022What Can Transformers Learn In-Context? A Case Study of Simple Function Classescited by 700
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  6. 2022Memory-Based Model Editing at Scalecited by 443
  7. 2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 429
  8. 2021Deep Long-Tailed Learning: A Surveycited by 416
  9. 2022Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classificationcited by 355
  10. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  11. 2021True Few-Shot Learning with Language Modelscited by 343
  12. 2022Visual Classification via Description from Large Language Modelscited by 331
38 more
  1. 2022Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Modelcited by 317
  2. 2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 310
  3. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  4. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  5. 2026Meta-Harness: End-to-End Optimization of Model Harnessescited by 248
  6. 2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 243
  7. 2023Improving CLIP Training with Language Rewritescited by 216
  8. 2023Class-Incremental Learning: A Surveycited by 209
  9. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  10. 2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
  11. 2021Trustworthy AI: From Principles to Practicescited by 206
  12. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
  13. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  14. 2021Few-Shot Incremental Learning with Continually Evolved Classifierscited by 184
  15. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  16. 2023Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Needcited by 183
  17. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  18. 2022Relative representations enable zero-shot latent space communicationcited by 175
  19. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  20. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  21. 2021PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clusteringcited by 163
  22. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  23. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  24. 2023Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learnerscited by 152
  25. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  26. 2021The Surprising Effectiveness of Representation Learning for Visual Imitationcited by 149
  27. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
  28. 2022Rethinking Semantic Segmentation: A Prototype Viewcited by 140
  29. 2021Hyperbolic Deep Neural Networks: A Surveycited by 138
  30. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  31. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  32. 2022Dataset Pruning: Reducing Training Data by Examining Generalization Influencecited by 132
  33. 2022Forward Compatible Few-Shot Class-Incremental Learningcited by 125
  34. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124
  35. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
  36. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  37. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  38. 2021Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minimacited by 118

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