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 976–1000 of 2964 papers

Title	Date	Tasks	Status
EPIC: Efficient Position-Independent Caching for Serving Large Language Models	Oct 20, 2024	ChunkingFew-Shot Learning	—Unverified
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions	Oct 19, 2024	DescriptiveFew-Shot Learning	—Unverified
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System	Oct 16, 2024	Domain AdaptationEEG	—Unverified
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning	Oct 14, 2024	Few-Shot LearningHyperparameter Optimization	—Unverified
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification	Oct 14, 2024	Data AugmentationFew-Shot Learning	CodeCode Available
KNN Transformer with Pyramid Prompts for Few-Shot Learning	Oct 14, 2024	Few-Shot Learning	—Unverified
Neural networks that overcome classic challenges through practice	Oct 14, 2024	Few-Shot LearningSystematic Generalization	—Unverified
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework	Oct 14, 2024	Few-Shot LearningTransfer Learning	—Unverified
Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition	Oct 13, 2024	Activity RecognitionFew-Shot Learning	—Unverified
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions	Oct 11, 2024	Few-Shot LearningImage Generation	—Unverified
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images	Oct 11, 2024	ClassificationFew-Shot Learning	—Unverified
Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity	Oct 11, 2024	Few-Shot Learning	—Unverified
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?	Oct 10, 2024	Few-Shot LearningIn-Context Learning	—Unverified
DemoShapley: Valuation of Demonstrations for In-Context Learning	Oct 10, 2024	FairnessFew-Shot Learning	—Unverified
Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial Literacy	Oct 9, 2024	Few-Shot LearningQuestion Answering	CodeCode Available
On The Relationship between Visual Anomaly-free and Anomalous Representations	Oct 9, 2024	Anomaly DetectionDomain Adaptation	—Unverified
DCP: Learning Accelerator Dataflow for Neural Network via Propagation	Oct 9, 2024	Few-Shot LearningScheduling	—Unverified
Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis	Oct 9, 2024	Few-Shot Learning	—Unverified
Manual Verbalizer Enrichment for Few-Shot Text Classification	Oct 8, 2024	BenchmarkingClassification	—Unverified
Generating Synthetic Datasets for Few-shot Prompt Tuning	Oct 8, 2024	Few-Shot LearningMRPC	—Unverified
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning	Oct 8, 2024	Few-Shot LearningHate Speech Detection	—Unverified
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing	Oct 8, 2024	Feature EngineeringFew-Shot Learning	—Unverified
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning	Oct 8, 2024	Contrastive LearningFew-Shot Learning	—Unverified
Revisiting In-context Learning Inference Circuit in Large Language Models	Oct 6, 2024	Few-Shot LearningIn-Context Learning	CodeCode Available
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition	Oct 6, 2024	Domain AdaptationEmotion Recognition	—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