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 1526–1550 of 2964 papers

Title	Date	Tasks	Status
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study	Jul 21, 2023	Few-Shot Learningtext-classification	—Unverified
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks	Jul 18, 2023	ClassificationFew-Shot Learning	—Unverified
A metric learning approach for endoscopic kidney stone identification	Jul 13, 2023	Few-Shot LearningKnowledge Distillation	—Unverified
TinyMetaFed: Efficient Federated Meta-Learning for TinyML	Jul 13, 2023	Computational EfficiencyFew-Shot Learning	—Unverified
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation	Jul 11, 2023	Few-Shot LearningImage Generation	—Unverified
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification	Jul 11, 2023	ClassificationContrastive Learning	—Unverified
Text Descriptions are Compressive and Invariant Representations for Visual Learning	Jul 10, 2023	DescriptiveFew-Shot Learning	—Unverified
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?	Jul 9, 2023	Contrastive LearningFew-Shot Image Classification	—Unverified
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?	Jul 6, 2023	Few-Shot LearningModel Selection	—Unverified
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition	Jul 5, 2023	Action RecognitionFew-Shot action recognition	CodeCode Available
TablEye: Seeing small Tables through the Lens of Images	Jul 4, 2023	Few-Shot Learning	—Unverified
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation	Jul 4, 2023	Active LearningBinary Classification	—Unverified
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking	Jul 4, 2023	Dialogue State TrackingFew-Shot Learning	CodeCode Available
On Conditional and Compositional Language Model Differentiable Prompting	Jul 4, 2023	Few-Shot LearningLanguage Modeling	—Unverified
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning	Jun 30, 2023	Few-Shot LearningGPU	—Unverified
Benchmarking Large Language Model Capabilities for Conditional Generation	Jun 29, 2023	BenchmarkingFew-Shot Learning	—Unverified
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection	Jun 28, 2023	BenchmarkingDiversity	—Unverified
Language models are weak learners	Jun 25, 2023	Few-Shot LearningLanguage Modelling	—Unverified
Is Pre-training Truly Better Than Meta-Learning?	Jun 24, 2023	DiversityFew-Shot Learning	—Unverified
Mutually Guided Few-shot Learning for Relational Triple Extraction	Jun 23, 2023	Cross-Domain Few-ShotDecoder	CodeCode Available
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair	Jun 21, 2023	Code RepairFew-Shot Learning	—Unverified
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN	Jun 21, 2023	Few-Shot Learning	CodeCode Available
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts	Jun 20, 2023	Few-Shot Learning	—Unverified
Visually grounded few-shot word learning in low-resource settings	Jun 20, 2023	Few-Shot Learning	—Unverified
Multilingual Few-Shot Learning via Language Model Retrieval	Jun 19, 2023	Few-Shot LearningIn-Context 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