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 576–600 of 2964 papers

Title	Date	Tasks	Status	Hype
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks	May 5, 2024	Few-Shot LearningMulti-agent Reinforcement Learning	—Unverified	0
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models	May 4, 2024	Few-Shot LearningText to SQL	—Unverified	0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning	May 4, 2024	ArticlesFew-Shot Learning	—Unverified	0
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model	May 3, 2024	Decision MakingFew-Shot Learning	CodeCode Available	2
A Survey of Few-Shot Learning for Biomedical Time Series	May 3, 2024	Few-Shot LearningTime Series	—Unverified	0
Accelerating Convergence in Bayesian Few-Shot Classification	May 2, 2024	ClassificationFew-Shot Learning	CodeCode Available	0
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation	May 2, 2024	Dataset GenerationDomain Generalization	CodeCode Available	0
A separability-based approach to quantifying generalization: which layer is best?	May 2, 2024	Domain AdaptationDomain Generalization	CodeCode Available	0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains	May 1, 2024	Bayesian InferenceFew-Shot Image Classification	—Unverified	0
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis	May 1, 2024	DeepFake DetectionFace Swapping	CodeCode Available	0
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition	Apr 30, 2024	3D Shape RecognitionFew-Shot Learning	—Unverified	0
StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation	Apr 30, 2024	Contrastive LearningFew-Shot Learning	CodeCode Available	0
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC	Apr 30, 2024	Few-Shot Learning	—Unverified	0
UniFS: Universal Few-shot Instance Perception with Point Representations	Apr 30, 2024	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	1
Ta-Adapter: Enhancing few-shot CLIP with task-aware encoders	Apr 29, 2024	Few-Shot Learningimage-classification	—Unverified	0
Certification of Speaker Recognition Models to Additive Perturbations	Apr 29, 2024	Few-Shot LearningSpeaker Recognition	CodeCode Available	0
Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language	Apr 27, 2024	Few-Shot LearningIn-Context Learning	—Unverified	0
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors	Apr 27, 2024	Few-Shot LearningIn-Context Learning	—Unverified	0
Empowering Large Language Models for Textual Data Augmentation	Apr 26, 2024	Data AugmentationDiversity	—Unverified	0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution	Apr 25, 2024	Data AugmentationFew-Shot Learning	—Unverified	0
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models	Apr 25, 2024	Data AugmentationDomain Generalization	CodeCode Available	1
Beyond Deepfake Images: Detecting AI-Generated Videos	Apr 24, 2024	Face SwappingFew-Shot Learning	—Unverified	0
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)	Apr 24, 2024	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Identifying Fairness Issues in Automatically Generated Testing Content	Apr 23, 2024	FairnessFew-Shot Learning	—Unverified	0
Graph Machine Learning in the Era of Large Language Models (LLMs)	Apr 23, 2024	Few-Shot LearningKnowledge Graphs	—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