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 2901–2925 of 2964 papers

Title	Date	Tasks	Status
Simulated Annealing in Early Layers Leads to Better Generalization	Apr 10, 2023	Few-Shot LearningTransfer Learning	CodeCode Available
Federated Few-shot Learning for Cough Classification with Edge Devices	Sep 3, 2023	Cough ClassificationFederated Learning	CodeCode Available
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification	Sep 29, 2024	ClassificationFew-Shot Learning	CodeCode Available
Boosting Point-BERT by Multi-choice Tokens	Jul 27, 2022	Few-Shot LearningLanguage Modeling	CodeCode Available
Few-Shot Learning with Localization in Realistic Settings	Apr 9, 2019	Few-Shot LearningMeta-Learning	CodeCode Available
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing	Sep 19, 2024	Few-Shot LearningSpike Sorting	CodeCode Available
Extensively Matching for Few-shot Learning Event Detection	Jun 17, 2020	Event DetectionFew-Shot Learning	CodeCode Available
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning	May 12, 2021	Few-Shot LearningMeta-Learning	CodeCode Available
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms	Oct 15, 2020	Few-Shot LearningMeta-Learning	CodeCode Available
Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks	Jun 1, 2019	ColorizationFew-Shot Learning	CodeCode Available
Universal Music Representations? Evaluating Foundation Models on World Music Corpora	Jun 20, 2025	BenchmarkingFew-Shot Learning	CodeCode Available
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization	May 3, 2023	Abstractive Text SummarizationContrastive Learning	CodeCode Available
Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial Literacy	Oct 9, 2024	Few-Shot LearningQuestion Answering	CodeCode Available
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection	Dec 14, 2021	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available
Can In-context Learners Learn a Reasoning Concept from Demonstrations?	Dec 3, 2022	Few-Shot LearningIn-Context Learning	CodeCode Available
Pragmatic Competence Evaluation of Large Language Models for the Korean Language	Mar 19, 2024	Few-Shot LearningMultiple-choice	CodeCode Available
CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings	Nov 15, 2021	Continual LearningFew-Shot Learning	CodeCode Available
Predicting the Accuracy of a Few-Shot Classifier	Jul 8, 2020	Few-Shot Learning	CodeCode Available
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning	Jun 8, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering	Feb 23, 2023	ClassificationDimensionality Reduction	CodeCode Available
Pre-Finetuning for Few-Shot Emotional Speech Recognition	Feb 24, 2023	Few-Shot Learningspeech-recognition	CodeCode Available
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization	Aug 7, 2022	ClassificationDiversity	CodeCode Available
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning	Dec 2, 2024	Abuse DetectionAbusive Language	CodeCode Available
Pre-trained Recommender Systems: A Causal Debiasing Perspective	Oct 30, 2023	Few-Shot LearningRecommendation Systems	CodeCode Available
Pre-trained Token-replaced Detection Model as Few-shot Learner	Mar 7, 2022	Few-Shot LearningSentence	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