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 876–900 of 2964 papers

Title	Date	Tasks	Status	Score
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection	Oct 6, 2020	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available	5
Unlocking the Full Potential of Small Data with Diverse Supervision	Nov 29, 2019	Few-Shot LearningRepresentation Learning	CodeCode Available	5
Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results	Sep 19, 2023	Few-Shot Learning	CodeCode Available	5
A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation	Oct 30, 2019	Few-Shot LearningSentence	CodeCode Available	5
Bootstrapped Meta-Learning	Sep 9, 2021	Efficient ExplorationFew-Shot Learning	CodeCode Available	5
Learning from the Tangram to Solve Mini Visual Tasks	Dec 12, 2021	Few-Shot Learning	CodeCode Available	5
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning	May 22, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	5
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification	Mar 4, 2022	counterfactualFew-Shot Learning	CodeCode Available	5
Towards Context-Agnostic Learning Using Synthetic Data	May 29, 2020	Few-Shot Learningimage-classification	CodeCode Available	5
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks	Nov 10, 2019	DiversityEntity Typing	CodeCode Available	5
Class-Agnostic Counting	Nov 1, 2018	Crowd CountingFew-Shot Learning	CodeCode Available	5
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction	Oct 6, 2021	Few-Shot LearningOne-Shot Learning	CodeCode Available	5
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder	Aug 11, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	5
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks	Feb 19, 2025	Few-Shot LearningGraph Learning	CodeCode Available	5
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging	Sep 17, 2024	Audio TaggingFew-Shot Learning	CodeCode Available	5
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning	Apr 17, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	5
LCNN: Lookup-based Convolutional Neural Network	Nov 20, 2016	Few-Shot Learning	CodeCode Available	5
LAVA: Label-efficient Visual Learning and Adaptation	Oct 19, 2022	Few-Shot LearningTransfer Learning	CodeCode Available	5
LaSO: Label-Set Operations networks for multi-label few-shot learning	Feb 26, 2019	Data AugmentationFew-Shot Learning	CodeCode Available	5
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning	Nov 10, 2024	Few-Shot Learning	CodeCode Available	5
learn2learn: A Library for Meta-Learning Research	Aug 27, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	5
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks	Mar 12, 2025	DiversityFew-Shot Learning	CodeCode Available	5
Large Language Models Vote: Prompting for Rare Disease Identification	Aug 24, 2023	Few-Shot Learning	CodeCode Available	5
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction	Feb 18, 2025	Aspect-Based Sentiment Analysis (ABSA)Few-Shot Learning	CodeCode Available	5
Do Tutors Learn from Equity Training and Can Generative AI Assess It?	Dec 15, 2024	Few-Shot Learning	CodeCode Available	5

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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