Few-shot Learning Publications

Publications in Few-shot Learning

Explore the most influential research papers and scholarly works that have shaped Few-shot Learning. Use the concept filter below to focus on a specific sub-area of research.

Few-shot Learning

4.2K

Publications

356.6K

Citations

Showing yearly statistics from 2000-2026.

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	Year	Citations
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn, Pieter Abbeel, Sergey Levine arXiv (Cornell University) Artificial IntelligenceFew-shot LearningFast AdaptationMachine VisionMachine Learning	2017	5.8K
Learning Transferable Visual Models From Natural Language Supervision Alec Radford, Jong Wook Kim, Chris Hallacy, arXiv (Cornell University) Few-shot LearningEngineeringMachine LearningNatural Language ProcessingMultimodal Llm	2021	5.3K
Prototypical Networks for Few-shot Learning Jake Snell, Kevin Swersky, Richard S. Zemel arXiv (Cornell University) Artificial IntelligenceFew-shot LearningEngineeringMachine LearningData Science	2017	5.2K
Unsupervised Feature Learning via Non-parametric Instance Discrimination Zhirong Wu, Yuanjun Xiong, Stella X. Yu, Few-shot LearningNeural Net ClassifiersMultiple Instance LearningEngineeringMachine Learning	2018	3.5K
Bootstrap your own latent: A new approach to self-supervised Learning Jean-Bastien Grill, Florian Strub, Florent Altché, arXiv (Cornell University) Artificial IntelligenceFew-shot LearningConvolutional Neural NetworkEngineeringMachine Learning	2020	3.4K
Siamese Neural Networks for One-shot Image Recognition Gregory Koch, Richard S. Zemel, Ruslan Salakhutdinov Few-shot LearningConvolutional Neural NetworkEngineeringMachine LearningImage Analysis	2015	3.2K
Exploring Simple Siamese Representation Learning Xinlei Chen, Kaiming He Natural Language ProcessingFew-shot LearningSiamese NetworksSiamese ArchitecturesMachine Vision	2021	3.2K
One-shot learning of object categories Li Fei-Fei, Rob Fergus, Pietro Perona IEEE Transactions on Pattern Analysis and Machine Intelligence Bayesian ImplementationFew-shot LearningEngineeringMachine LearningObject Categorization	2006	3K
Language Models are Few-Shot Learners T. B. Brown, Benjamin Mann, Nick Ryder, arXiv (Cornell University) Artificial IntelligenceFew-shot LearningLlm Fine-tuningEngineeringMachine Learning	2020	3K
Unsupervised Domain Adaptation by Backpropagation Yaroslav Ganin, Victor Lempitsky arXiv (Cornell University) Few-shot LearningTop-performing Deep ArchitecturesMachine VisionMachine LearningData Science	2014	2.6K
Optimization as a Model for Few-Shot Learning Sachin Ravi, Hugo Larochelle International Conference on Learning Representations Artificial IntelligenceFew-shot LearningLearner Neural NetworkDeep Neural NetworksGeneral Initialization	2017	2.4K
Deep Domain Confusion: Maximizing for Domain Invariance Eric Tzeng, Judy Hoffman, Ning Zhang, arXiv (Cornell University) Domain ConfusionFew-shot LearningConvolutional Neural NetworkImage AnalysisMachine Learning	2014	2.3K

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