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 751–775 of 2964 papers

Title	Date	Tasks	Status
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code	Jun 2, 2022	Code GenerationFew-Shot Learning	—Unverified
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation	Aug 17, 2023	Code GenerationFew-Shot Learning	—Unverified
CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt	Nov 13, 2024	ClassificationCode Completion	—Unverified
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class	Aug 12, 2024	ClassificationFew-Shot Image Classification	—Unverified
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving	Feb 17, 2023	Autonomous DrivingFew-Shot Learning	—Unverified
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization	Oct 13, 2020	Action RecognitionFew-Shot action recognition	—Unverified
Few-Shot Batch Incremental Road Object Detection via Detector Fusion	Aug 18, 2021	Few-Shot Learningobject-detection	—Unverified
A Framework of Meta Functional Learning for Regularising Knowledge Transfer	Mar 28, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation	Dec 23, 2024	DiversityFew-Shot Learning	—Unverified
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding	Jul 11, 2020	ClassificationFew-Shot Learning	—Unverified
A Similarity Paradigm Through Textual Regularization Without Forgetting	Feb 20, 2025	Domain GeneralizationFew-Shot Learning	—Unverified
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks	Dec 16, 2022	Federated LearningFew-Shot Learning	—Unverified
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning	Jan 1, 2021	Few-Shot LearningPseudo Label	—Unverified
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification	Nov 16, 2021	Few-Shot LearningFew-Shot Text Classification	—Unverified
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite	Mar 18, 2024	Contrastive LearningData Augmentation	—Unverified
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks	Jan 28, 2025	Few-Shot LearningSelf-Supervised Learning	—Unverified
Few-Round Learning for Federated Learning	Jan 1, 2021	Federated LearningFew-Shot Learning	—Unverified
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs	Nov 9, 2024	ClassificationClustering	—Unverified
Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering	May 22, 2018	ClusteringFew-Shot Learning	—Unverified
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning	Mar 10, 2024	Federated LearningFew-Shot Learning	—Unverified
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping	Feb 28, 2023	3D Point Cloud ClassificationContrastive Learning	—Unverified
Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning	Oct 11, 2023	Argument MiningFew-Shot Learning	—Unverified
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions	May 13, 2025	Federated LearningFew-Shot Learning	—Unverified
A Broad Dataset is All You Need for One-Shot Object Detection	Nov 9, 2020	AllFew-Shot Learning	—Unverified
A Nested Bi-level Optimization Framework for Robust Few Shot Learning	Nov 13, 2020	Few-Shot LearningMeta-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