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 1351–1375 of 2964 papers

Title	Date	Tasks	Status	Hype
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective	Nov 2, 2022	Few-Shot Learning	—Unverified	0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization	Nov 1, 2022	Binary ClassificationDomain Generalization	—Unverified	0
SAGE: Saliency-Guided Mixup with Optimal Rearrangements	Oct 31, 2022	Data AugmentationDomain Generalization	—Unverified	0
GPS: Genetic Prompt Search for Efficient Few-shot Learning	Oct 31, 2022	Few-Shot Learning	CodeCode Available	0
Learning New Tasks from a Few Examples with Soft-Label Prototypes	Oct 31, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative Embeddings	Oct 30, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid	Oct 30, 2022	Few-Shot LearningSelection bias	CodeCode Available	1
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition	Oct 30, 2022	Action RecognitionDynamic Time Warping	—Unverified	0
STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification	Oct 29, 2022	Few-Shot LearningFew-Shot Text Classification	—Unverified	0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships	Oct 27, 2022	Domain AdaptationFew-Shot Learning	CodeCode Available	0
Efficient few-shot learning for pixel-precise handwritten document layout analysis	Oct 27, 2022	Document Layout AnalysisFew-Shot Learning	—Unverified	0
Dictionary-Assisted Supervised Contrastive Learning	Oct 27, 2022	Contrastive LearningData Augmentation	CodeCode Available	0
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting	Oct 27, 2022	Face RecognitionFew-Shot Learning	CodeCode Available	0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES	Oct 25, 2022	Federated LearningFew-Shot Learning	—Unverified	0
Better Few-Shot Relation Extraction with Label Prompt Dropout	Oct 25, 2022	Few-Shot LearningRelation	CodeCode Available	1
TAPE: Assessing Few-shot Russian Language Understanding	Oct 23, 2022	Adversarial AttackAdversarial Text	CodeCode Available	0
A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning	Oct 22, 2022	Few-Shot Learning	CodeCode Available	0
Contrastive Prototypical Network with Wasserstein Confidence Penalty	Oct 21, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
Graph Few-shot Learning with Task-specific Structures	Oct 21, 2022	ClassificationFew-Shot Learning	CodeCode Available	0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification	Oct 20, 2022	ClassificationContrastive Learning	—Unverified	0
Self-Supervised Representation Learning for CAD	Oct 19, 2022	Few-Shot LearningRepresentation Learning	—Unverified	0
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models	Oct 19, 2022	Few-Shot Learningparameter-efficient fine-tuning	—Unverified	0
LAVA: Label-efficient Visual Learning and Adaptation	Oct 19, 2022	Few-Shot LearningTransfer Learning	CodeCode Available	0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency	Oct 19, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Aligning MAGMA by Few-Shot Learning and Finetuning	Oct 18, 2022	Few-Shot LearningImage Captioning	—Unverified	0

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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