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 2001–2025 of 2964 papers

Title	Date	Tasks	Status
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network	Jan 6, 2025	3D Object ClassificationFew-Shot Learning	—Unverified
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation	Oct 3, 2024	Bilevel OptimizationData Augmentation	—Unverified
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning	Jul 3, 2024	Few-Shot LearningGeneral Knowledge	—Unverified
SAGE: Saliency-Guided Mixup with Optimal Rearrangements	Oct 31, 2022	Data AugmentationDomain Generalization	—Unverified
SAMIC: Segment Anything with In-Context Spatial Prompt Engineering	Dec 16, 2024	Few-Shot LearningPrompt Engineering	—Unverified
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation	Dec 15, 2024	DecoderFew-shot Instance Segmentation	—Unverified
SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting	Nov 26, 2024	Few-Shot LearningImage Segmentation	—Unverified
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations	Jan 27, 2025	Few-Shot LearningRecommendation Systems	—Unverified
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation	Jul 11, 2023	Few-Shot LearningImage Generation	—Unverified
SaSi: A Self-augmented and Self-interpreted Deep Learning Approach for Few-shot Cryo-ET Particle Detection	May 26, 2025	Electron TomographyFew-Shot Learning	—Unverified
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning	Dec 3, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Scalable Prompt Generation for Semi-supervised Learning with Language Models	Feb 18, 2023	Few-Shot LearningNatural Language Understanding	—Unverified
Scaling ASR Improves Zero and Few Shot Learning	Nov 10, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers	Oct 13, 2021	Few-Shot Learningimage-classification	—Unverified
Scaling Vision Transformers	Jun 8, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions	Oct 11, 2024	Few-Shot LearningImage Generation	—Unverified
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images	May 22, 2025	Anomaly DetectionFew-Shot Learning	—Unverified
Search-based Optimisation of LLM Learning Shots for Story Point Estimation	Mar 13, 2024	Few-Shot LearningMeta-Learning	—Unverified
Second-order contexts from lexical substitutes for few-shot learning of word representations	Jun 1, 2019	Few-Shot LearningSentence	—Unverified
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging	Mar 25, 2024	Few-Shot LearningImage Segmentation	—Unverified
Segmentation of Knee Bones for Osteoarthritis Assessment: A Comparative Analysis of Supervised, Few-Shot, and Zero-Shot Learning Approaches	Mar 13, 2024	Few-Shot LearningMorphological Analysis	—Unverified
Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net	Jul 29, 2024	DecoderFew-Shot Learning	—Unverified
Selecting Between BERT and GPT for Text Classification in Political Science Research	Nov 7, 2024	Few-Shot LearningPrompt Engineering	—Unverified
Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models	Nov 14, 2023	FairnessFew-Shot Learning	—Unverified
Selecting task with optimal transport self-supervised learning for few-shot classification	Apr 1, 2022	Data AugmentationFew-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