Few-Shot Learning

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

Papers

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Showing 1501–1525 of 2964 papers

Title	Date	Tasks	Status
Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning	Oct 11, 2023	Deep Reinforcement LearningFew-Shot Learning	—Unverified
Explaining CLIP's performance disparities on data from blind/low vision users	Nov 29, 2023	Few-Shot Learningzero-shot-classification	—Unverified
Explaining Representation by Mutual Information	Mar 28, 2021	BIG-bench Machine LearningFew-Shot Learning	—Unverified
Explicit Knowledge Transfer for Weakly-Supervised Code Generation	Nov 30, 2022	Code GenerationFew-Shot Learning	—Unverified
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task	May 1, 2022	Few-Shot LearningIn-Context Learning	—Unverified
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning	Jan 19, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Exploiting the Matching Information in the Support Set for Few Shot Event Classification	Feb 13, 2020	ClassificationFew-Shot Learning	—Unverified
Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners	Jul 27, 2023	DecoderFew-Shot Learning	—Unverified
Explore the Power of Dropout on Few-shot Learning	Jan 26, 2023	Deep LearningFew-Shot Image Classification	—Unverified
Exploring Data Augmentation Methods on Social Media Corpora	Mar 3, 2023	Data AugmentationFew-Shot Learning	—Unverified
Exploring Example Selection for Few-shot Text-to-SQL Semantic Parsing	Jan 16, 2022	Few-Shot LearningIn-Context Learning	—Unverified
Exploring Factual Entailment with NLI: A News Media Study	Jun 24, 2024	ArticlesFew-Shot Learning	—Unverified
Exploring Generative AI Techniques in Government: A Case Study	Apr 6, 2025	Few-Shot LearningManagement	—Unverified
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study	Nov 21, 2023	Data AugmentationFew-Shot Learning	—Unverified
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects	Apr 29, 2025	Contrastive LearningFew-Shot Learning	—Unverified
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning	Feb 6, 2024	Few-Shot Learningimage-classification	—Unverified
Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation	Sep 14, 2021	Few-Shot Learning	—Unverified
Exploring representation learning for flexible few-shot tasks	Jan 1, 2021	Few-Shot LearningRepresentation Learning	—Unverified
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models	Sep 4, 2024	Decision MakingFew-Shot Learning	—Unverified
Exploring structure diversity in atomic resolution microscopy with graph neural networks	Oct 23, 2024	DiversityFew-Shot Learning	—Unverified
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding	Apr 2, 2025	Activity RecognitionFew-Shot Learning	—Unverified
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases	Mar 25, 2024	Few-Shot Learning	—Unverified
Exploring the LLM Journey from Cognition to Expression with Linear Representations	May 27, 2024	Few-Shot Learning	—Unverified
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models	Jun 15, 2023	Electrical EngineeringFew-Shot Learning	—Unverified
Exploring the Space of Key-Value-Query Models with Intention	May 17, 2023	Deep LearningFew-Shot Learning	—Unverified

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All datasets MedConceptsQA DTD FGVC-Aircraft Mini-ImageNet - 5-Shot Learning Mini-Imagenet 5-way (1-shot)Stanford Cars Mini-ImageNet - 1-Shot Learning PubMedQA Caltech101 CaseHOLD EuroSAT Flowers-102

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	gpt-4-0125-preview	Accuracy	61.91	—	Unverified
2	gpt-4-0125-preview	Accuracy	52.49	—	Unverified
3	gpt-3.5-turbo	Accuracy	41.48	—	Unverified
4	gpt-3.5-turbo	Accuracy	37.06	—	Unverified
5	johnsnowlabs/JSL-MedMNX-7B	Accuracy	25.63	—	Unverified
6	yikuan8/Clinical-Longformer	Accuracy	25.55	—	Unverified
7	BioMistral/BioMistral-7B-DARE	Accuracy	25.06	—	Unverified
8	yikuan8/Clinical-Longformer	Accuracy	25.04	—	Unverified
9	PharMolix/BioMedGPT-LM-7B	Accuracy	24.92	—	Unverified
10	PharMolix/BioMedGPT-LM-7B	Accuracy	24.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	67.27	—	Unverified
2	SaSPA + CAL	4-shot Accuracy	48.3	—	Unverified
3	Real-Guidance + CAL	4-shot Accuracy	41.5	—	Unverified
4	CAL	4-shot Accuracy	40.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SaSPA + CAL	Harmonic mean	52.2	—	Unverified
2	CAL	Harmonic mean	35.2	—	Unverified
3	Variational Prompt Tuning	Harmonic mean	34.69	—	Unverified
4	Real-Guidance + CAL	Harmonic mean	34.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BGNN	Accuracy	92.7	—	Unverified
2	TIM-GD	Accuracy	87.4	—	Unverified
3	UNEM-Gaussian	Accuracy	66.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EASY (transductive)	Accuracy	82.75	—	Unverified
2	HCTransformers	5 way 1~2 shot	74.74	—	Unverified
3	HyperShot	Accuracy	53.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SaSPA + CAL	4-shot Accuracy	66.7	—	Unverified
2	Real-Guidance + CAL	4-shot Accuracy	44.3	—	Unverified
3	CAL	4-shot Accuracy	42.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HCTransformers	Acc	74.74	—	Unverified
2	DPGN	Acc	67.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MetaGen Blended RAG (zero-shot)	Accuracy	77.9	—	Unverified
2	CoT-T5-11B (1024 Shot)	Accuracy	73.42	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	96.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CoT-T5-11B (1024 Shot)	Accuracy	68.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	77.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	81.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	91.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CovidExpert	AUC-ROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CoT-T5-11B (1024 Shot)	Accuracy	78.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	65.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	73.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	96.82	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	73.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	78.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	52.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	79	—	Unverified