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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2501–2525 of 2964 papers

Title	Date	Tasks	Status
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution	Jan 14, 2025	Adversarial RobustnessAttribute	—Unverified
ICDAR 2024 Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts (SAM)	Sep 11, 2024	DiversityDocument Layout Analysis	—Unverified
Identification of emotions on Twitter during the 2022 electoral process in Colombia	Jul 9, 2024	Few-Shot LearningOpinion Mining	—Unverified
Identifying Fairness Issues in Automatically Generated Testing Content	Apr 23, 2024	FairnessFew-Shot Learning	—Unverified
Identity Document to Selfie Face Matching Across Adolescence	Dec 20, 2019	Few-Shot LearningTriplet	—Unverified
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System	Oct 16, 2024	Domain AdaptationEEG	—Unverified
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications	Sep 2, 2022	Contrastive LearningDiversity	—Unverified
Impact of Aliasing on Generalization in Deep Convolutional Networks	Aug 7, 2021	Data AugmentationFew-Shot Learning	—Unverified
Impossible Triangle: What's Next for Pre-trained Language Models?	Apr 13, 2022	Data AugmentationFew-Shot Learning	—Unverified
Improved Few-Shot Visual Classification	Dec 7, 2019	ClassificationFew-Shot Image Classification	—Unverified
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases	May 26, 2025	Few-Shot Learningimage-classification	—Unverified
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning	Jan 1, 2021	Few-Shot Learning	—Unverified
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations	Nov 27, 2023	DenoisingFew-Shot Learning	—Unverified
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning	Oct 25, 2023	Data AugmentationFew-Shot Learning	—Unverified
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task	Jun 29, 2020	ClassificationFew-Shot Learning	—Unverified
Improving Few-shot Learning with Weakly-supervised Object Localization	May 25, 2021	Few-Shot LearningMetric Learning	—Unverified
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration	Dec 5, 2022	Few-Shot LearningFew-Shot Text Classification	—Unverified
Enhancing Few-Shot Image Classification with Unlabelled Examples	Jun 17, 2020	ClassificationClustering	—Unverified
Improving Few-Shot Visual Classification with Unlabelled Examples	Sep 28, 2020	ClassificationClustering	—Unverified
Improving In-Context Few-Shot Learning via Self-Supervised Training	May 3, 2022	DiversityFew-Shot Learning	—Unverified
Improving Instruct Models for Free: A Study on Partial Adaptation	Apr 15, 2025	Few-Shot LearningIn-Context Learning	—Unverified
Improving out-of-distribution generalization via multi-task self-supervised pretraining	Mar 30, 2020	Adversarial RobustnessDomain Generalization	—Unverified
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction	Apr 15, 2024	Few-Shot Learning	—Unverified
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target	May 29, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
In-BoXBART: Get Instructions into Biomedical Multi-task Learning	Jan 16, 2022	Few-Shot LearningMulti-Task 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