Data to 7 Oct 2026

Matching Networks for One Shot Learning

arXiv 1606.04080 ↗First posted 13 Jun 2016cs.LG stat.MLcited by 2,246

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Cited by 38 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.

Key facts. Cited by 2,246 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2016.

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Citing papers per year: 2018: 4; 2019: 1; 2020: 2; 2021: 579; 2022: 527; 2023: 460; 2024: 321; 2025: 234; 2026: 118.

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2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 657
2022Memory-Based Model Editing at Scalecited by 443
2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 429
2022Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classificationcited by 355
2021True Few-Shot Learning with Language Modelscited by 343
2021Gradient Projection Memory for Continual Learningcited by 338
2021Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scanscited by 336
2022Visual Classification via Description from Large Language Modelscited by 331
2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 310
2022Data Distributional Properties Drive Emergent In-Context Learning in Transformerscited by 303

38 more

2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 243
2021Online Continual Learning in Image Classification: An Empirical Surveycited by 242
2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
2021Few-Shot Incremental Learning with Continually Evolved Classifierscited by 184
2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
2023Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learnerscited by 152
2021Learning To Count Everythingcited by 149
2022Prompting Decision Transformer for Few-Shot Policy Generalizationcited by 143
2023ImageBind-LLM: Multi-modality Instruction Tuningcited by 142
2021Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learningcited by 140
2022Rethinking Semantic Segmentation: A Prototype Viewcited by 140
2021Hyperbolic Deep Neural Networks: A Surveycited by 138
2022Dataset Pruning: Reducing Training Data by Examining Generalization Influencecited by 132
2022Forward Compatible Few-Shot Class-Incremental Learningcited by 125
2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
2022Class-Incremental Continual Learning into the eXtended DER-versecited by 121
2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
2021Instance-Conditioned GANcited by 119
2021Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minimacited by 118
2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
2021Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Biascited by 115
2021Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learningcited by 114
2021Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samplescited by 114
2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
2021Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learningcited by 112
2023Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learningcited by 112
2023Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entitiescited by 112
2022Efficient Dataset Distillation Using Random Feature Approximationcited by 111
2021PartImageNet: A Large, High-Quality Dataset of Partscited by 107
2021A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentumcited by 106

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