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 851–875 of 2964 papers

Title	Date	Tasks	Status
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks	Jan 28, 2025	Few-Shot LearningSelf-Supervised Learning	—Unverified
Distilling Large Language Models for Network Active Queue Management	Jan 28, 2025	Few-Shot LearningManagement	—Unverified
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies	Jan 28, 2025	Few-Shot LearningFew-Shot Semantic Segmentation	—Unverified
Closed-Form Feedback-Free Learning with Forward Projection	Jan 27, 2025	Few-Shot LearningForm	CodeCode Available
Evaluating Data Influence in Meta Learning	Jan 27, 2025	Bilevel OptimizationComputational Efficiency	—Unverified
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations	Jan 27, 2025	Few-Shot LearningRecommendation Systems	—Unverified
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification	Jan 25, 2025	Few-Shot Learningparameter-efficient fine-tuning	—Unverified
Geometric Mean Improves Loss For Few-Shot Learning	Jan 24, 2025	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Evaluating and Improving Graph to Text Generation with Large Language Models	Jan 24, 2025	DiversityFew-Shot Learning	CodeCode Available
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education	Jan 24, 2025	Few-Shot LearningLanguage Modeling	CodeCode Available
CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information Injection and Restricted Pseudo Labeling based Improved Semi-Supervised Few-Shot Learning	Jan 24, 2025	ClusteringFew-Shot Learning	—Unverified
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs	Jan 23, 2025	DiagnosticFew-Shot Learning	—Unverified
Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models	Jan 23, 2025	Few-Shot LearningIn-Context Learning	—Unverified
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility	Jan 23, 2025	Data AugmentationDomain Adaptation	—Unverified
Text-driven Online Action Detection	Jan 23, 2025	Action DetectionAutonomous Driving	CodeCode Available
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
Patent Figure Classification using Large Vision-language Models	Jan 22, 2025	ClassificationFew-Shot Learning	CodeCode Available
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis	Jan 22, 2025	Cancer ClassificationComputed Tomography (CT)	CodeCode Available
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking	Jan 18, 2025	Binary ClassificationFact Checking	—Unverified
ACE: Anatomically Consistent Embeddings in Composition and Decomposition	Jan 17, 2025	Few-Shot LearningImage Cropping	CodeCode Available
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning	Jan 16, 2025	Contrastive LearningFew-Shot Learning	—Unverified
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution	Jan 14, 2025	Adversarial RobustnessAttribute	—Unverified
An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering	Jan 12, 2025	Few-Shot LearningIn-Context Learning	—Unverified
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context	Jan 12, 2025	Binary ClassificationDiagnostic	—Unverified
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport	Jan 10, 2025	Few-Shot Learning	CodeCode Available

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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