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 901–925 of 2964 papers

Title	Date	Tasks	Status	Score
Do Tutors Learn from Equity Training and Can Generative AI Assess It?	Dec 15, 2024	Few-Shot Learning	CodeCode Available	5
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	5
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging	Sep 17, 2024	Audio TaggingFew-Shot Learning	CodeCode Available	5
learn2learn: A Library for Meta-Learning Research	Aug 27, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	5
Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial Literacy	Oct 9, 2024	Few-Shot LearningQuestion Answering	CodeCode Available	5
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning	May 12, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	5
Boosting keyword spotting through on-device learnable user speech characteristics	Mar 12, 2024	Few-Shot LearningKeyword Spotting	CodeCode Available	5
Do Prompt-Based Models Really Understand the Meaning of their Prompts?	Sep 2, 2021	Few-Shot LearningNatural Language Inference	CodeCode Available	5
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning	Apr 17, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	5
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning	Oct 25, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild	May 13, 2016	Few-Shot LearningGeneralized Zero-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
LCNN: Lookup-based Convolutional Neural Network	Nov 20, 2016	Few-Shot Learning	CodeCode Available	5
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning	Dec 20, 2024	Domain AdaptationFew-Shot Learning	CodeCode Available	5
Are Large Language Models Robust Coreference Resolvers?	May 23, 2023	coreference-resolutionCoreference Resolution	CodeCode Available	5
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	5
Boosting Few-Shot Visual Learning with Self-Supervision	Jun 12, 2019	Few-Shot LearningSelf-Supervised Learning	CodeCode Available	5
Large Language Models Vote: Prompting for Rare Disease Identification	Aug 24, 2023	Few-Shot Learning	CodeCode Available	5
Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy	Jun 1, 2019	Few-Shot LearningTransfer Learning	CodeCode Available	5
Large Language Models as Attribution Regularizers for Efficient Model Training	Feb 27, 2025	Few-Shot Learning	CodeCode Available	5
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification	Sep 29, 2024	ClassificationFew-Shot 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
Does MAML Only Work via Feature Re-use? A Data Centric Perspective	Dec 24, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	5
Does language help generalization in vision models?	Apr 16, 2021	Adversarial RobustnessClustering	CodeCode Available	5
An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of Domains	Aug 15, 2024	Few-Shot LearningTransfer 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