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 2201–2225 of 2964 papers

Title	Date	Tasks	Status
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
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery	Jul 22, 2021	Few-Shot LearningZero-Shot Learning	—Unverified
EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome	May 13, 2022	Autonomous DrivingDecision Making	—Unverified
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network	Oct 15, 2019	Emotion RecognitionFew-Shot Learning	—Unverified
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition	Mar 24, 2025	Domain AdaptationEEG	—Unverified
Facial Landmark Correlation Analysis	Nov 24, 2019	Facial Landmark DetectionFew-Shot Learning	—Unverified
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning	May 13, 2025	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Fair Few-shot Learning with Auxiliary Sets	Aug 28, 2023	FairnessFew-Shot Learning	—Unverified
Fast Adaptation with Kernel and Gradient based Meta Leaning	Nov 1, 2024	Few-Shot LearningMeta-Learning	—Unverified
Generalized Adaptation for Few-Shot Learning	Nov 25, 2019	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Fast Task Adaptation for Few-Shot Learning	Sep 25, 2019	Few-Shot Learning	—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
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules	Aug 26, 2024	ClassificationFew-Shot Image Classification	—Unverified
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification	May 18, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Federated Few-Shot Learning with Adversarial Learning	Apr 1, 2021	Federated LearningFew-Shot Learning	—Unverified
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions	May 13, 2025	Federated LearningFew-Shot Learning	—Unverified
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification	Oct 21, 2024	Federated LearningFew-Shot Learning	—Unverified
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning	Mar 10, 2024	Federated 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