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 1976–2000 of 2964 papers

Title	Date	Tasks	Status
Supervised Momentum Contrastive Learning for Few-Shot Classification	Jan 26, 2021	ClassificationContrastive Learning	—Unverified
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification	Nov 28, 2022	Few-Shot LearningLanguage Modeling	—Unverified
Revisiting Few-Shot Learning for Facial Expression Recognition	Dec 5, 2019	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes	Apr 17, 2021	ClassificationFew-Shot Learning	—Unverified
Revisiting Fine-tuning for Few-shot Learning	Oct 1, 2019	Category-Agnostic Pose EstimationFew-Shot Learning	—Unverified
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning	Jan 1, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks	Sep 29, 2021	Few-Shot Learningimage-classification	—Unverified
Revisiting Meta-Learning as Supervised Learning	Feb 3, 2020	Data AugmentationFew-Shot Learning	—Unverified
Revisiting Metric Learning for Few-Shot Image Classification	Jul 6, 2019	ClassificationFew-Shot Image Classification	—Unverified
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition	Aug 7, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Revisiting the Exit from Nuclear Energy in Germany with NLP	Aug 25, 2024	Few-Shot Learning	—Unverified
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples	Sep 26, 2024	Few-Shot LearningMalware Detection	—Unverified
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning	Jul 1, 2022	Few-Shot LearningLink Prediction	—Unverified
Rich Semantics Improve Few-shot Learning	Apr 26, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Riemannian Geometric-based Meta Learning	Mar 14, 2025	Few-Shot LearningMeta-Learning	—Unverified
RmGPT: Rotating Machinery Generative Pretrained Model	Sep 26, 2024	Few-Shot Learningmodel	—Unverified
RM-PoT: Reformulating Mathematical Problems and Solving via Program of Thoughts	Feb 18, 2025	Few-Shot Learning	—Unverified
RNNP: A Robust Few-Shot Learning Approach	Nov 22, 2020	Few-Shot Learning	—Unverified
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control	Feb 5, 2025	Few-Shot LearningImitation Learning	—Unverified
Robust Cross-Modal Semi-supervised Few Shot Learning	Sep 29, 2021	Few-Shot LearningGenerative Adversarial Network	—Unverified
Robust Internal Representations for Domain Generalization	Sep 27, 2023	Continual LearningDomain Adaptation	—Unverified
Robust Meta-learning with Noise via Eigen-Reptile	Jan 1, 2021	Few-Shot LearningMeta-Learning	—Unverified
Robust Task Clustering for Deep Many-Task Learning	Aug 26, 2017	ClusteringFew-Shot Learning	—Unverified
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation	Nov 28, 2023	Few-Shot Learning	—Unverified
RPLKG: Robust Prompt Learning with Knowledge Graph	Apr 21, 2023	Domain GeneralizationFew-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