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 726–750 of 2964 papers

Title	Date	Tasks	Status
Evolution imposes an inductive bias that alters and accelerates learning dynamics	May 15, 2025	Few-Shot LearningInductive Bias	—Unverified
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark	Apr 6, 2021	Few-Shot LearningGeneral Classification	—Unverified
Assessing two novel distance-based loss functions for few-shot image classification	Sep 29, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning	Mar 10, 2024	Federated LearningFew-Shot Learning	—Unverified
Compare learning: bi-attention network for few-shot learning	Mar 25, 2022	Few-Shot LearningMetric Learning	—Unverified
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification	Nov 23, 2024	Binary ClassificationBrain Tumor Classification	—Unverified
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images	Nov 14, 2024	AnatomyFew-Shot Learning	—Unverified
Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations	Dec 5, 2023	Explanation GenerationFew-Shot Learning	—Unverified
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning	Jun 19, 2024	AllFew-Shot Learning	—Unverified
AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs	Jul 29, 2024	Few-Shot LearningIn-Context Learning	—Unverified
Combat Data Shift in Few-shot Learning with Knowledge Graph	Jan 27, 2021	Few-Shot LearningMeta-Learning	—Unverified
Aspect-Based Few-Shot Learning	Dec 17, 2024	Few-Shot Learning	—Unverified
Fast Task Adaptation for Few-Shot Learning	Sep 25, 2019	Few-Shot Learning	—Unverified
Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in Manufacturing Quality Control: An Expository Case Study with Multiple Application Examples	Jan 22, 2025	Few-Shot Learningimage-classification	—Unverified
Adapting Language-Audio Models as Few-Shot Audio Learners	May 28, 2023	Audio ClassificationClassification	—Unverified
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot	Jun 1, 2020	Few-Shot LearningTransfer Learning	—Unverified
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification	Jul 11, 2023	ClassificationContrastive Learning	—Unverified
Collaboration of Pre-trained Models Makes Better Few-shot Learner	Sep 25, 2022	Few-Shot LearningRepresentation Learning	—Unverified
A general-purpose AI assistant embedded in an open-source radiology information system	Mar 18, 2023	Active LearningFew-Shot Learning	—Unverified
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study	Jul 21, 2023	Few-Shot Learningtext-classification	—Unverified
Supervised Graph Contrastive Learning for Few-shot Node Classification	Mar 29, 2022	ClassificationContrastive Learning	—Unverified
A Game-Theoretic Perspective of Generalization in Reinforcement Learning	Aug 7, 2022	Few-Shot LearningMeta-Learning	—Unverified
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

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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