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 1826–1850 of 2964 papers

Title	Date	Tasks	Status
Aspect-Based Few-Shot Learning	Dec 17, 2024	Few-Shot Learning	—Unverified
Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations	Dec 5, 2023	Explanation GenerationFew-Shot Learning	—Unverified
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images	Nov 14, 2024	AnatomyFew-Shot Learning	—Unverified
Assessing two novel distance-based loss functions for few-shot image classification	Sep 29, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Associative Adversarial Learning Based on Selective Attack	Dec 28, 2021	Adversarial RobustnessFew-Shot Learning	—Unverified
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation	Feb 26, 2019	Data AugmentationFew-Shot Learning	—Unverified
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning	Oct 14, 2024	Few-Shot LearningHyperparameter Optimization	—Unverified
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning	Oct 21, 2021	Few-Shot Learning	—Unverified
A Study of Few-Shot Audio Classification	Dec 2, 2020	Audio ClassificationBIG-bench Machine Learning	—Unverified
A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems	Apr 18, 2022	Few-Shot LearningLanguage Modelling	—Unverified
A Study on Representation Transfer for Few-Shot Learning	Sep 5, 2022	Classificationfeature selection	—Unverified
A Survey of Deep Learning for Low-Shot Object Detection	Dec 6, 2021	Deep LearningFew-Shot Learning	—Unverified
A Survey of Deep Visual Cross-Domain Few-Shot Learning	Mar 16, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
A Survey of Few-Shot Learning for Biomedical Time Series	May 3, 2024	Few-Shot LearningTime Series	—Unverified
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge	Jul 29, 2022	Active LearningContrastive Learning	—Unverified
A Survey on Few-Shot Class-Incremental Learning	Apr 17, 2023	class-incremental learningClass Incremental Learning	—Unverified
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge	Jan 3, 2023	Few-Shot LearningGraph Representation Learning	—Unverified
A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods	Oct 29, 2023	ClassificationFew-Shot Learning	—Unverified
Asymmetric Distribution Measure for Few-shot Learning	Feb 1, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
A Systematic Review of Few-Shot Learning in Medical Imaging	Sep 20, 2023	ArticlesFew-Shot Learning	—Unverified
A Theoretical Analysis of the Number of Shots in Few-Shot Learning	Sep 25, 2019	ClassificationFew-Shot Learning	—Unverified
A theoretically grounded characterization of feature representations	Sep 29, 2021	Few-Shot LearningSelf-Supervised Learning	—Unverified
A Theory of Human-Like Few-Shot Learning	Jan 3, 2023	Common Sense ReasoningFew-Shot Learning	—Unverified
A Theory of Self-Supervised Framework for Few-Shot Learning	Jan 1, 2021	Few-Shot LearningSelf-Supervised Learning	—Unverified
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer	Nov 29, 2023	Few-Shot LearningTransductive 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