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 1801–1825 of 2964 papers

Title	Date	Tasks	Status
An evaluation of GPT models for phenotype concept recognition	Sep 29, 2023	Few-Shot LearningSpecificity	—Unverified
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State	Mar 17, 2021	Brain Computer InterfaceClassification	—Unverified
Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks	Oct 19, 2023	Few-Shot LearningIn-Context Learning	—Unverified
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations	Jun 24, 2024	AutoMLFew-Shot Learning	—Unverified
Annotation-Efficient Untrimmed Video Action Recognition	Nov 30, 2020	Action RecognitionContrastive Learning	—Unverified
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning	Dec 12, 2021	Anomaly DetectionCross-Domain Few-Shot	—Unverified
A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications	Dec 3, 2024	Few-Shot Learning	—Unverified
AnyTrans: Translate AnyText in the Image with Large Scale Models	Jun 17, 2024	Few-Shot LearningTranslation	—Unverified
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis	Jun 2, 2024	Few-Shot LearningPrompt Engineering	—Unverified
Applying Large Language Models and Chain-of-Thought for Automatic Scoring	Nov 30, 2023	Few-Shot LearningPrompt Engineering	—Unverified
Approximating Human-Like Few-shot Learning with GPT-based Compression	Aug 14, 2023	Data CompressionFew-Shot Learning	—Unverified
A Practical Guide to Fine-tuning Language Models with Limited Data	Nov 14, 2024	Few-Shot LearningTransfer Learning	—Unverified
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions	Oct 19, 2024	DescriptiveFew-Shot Learning	—Unverified
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing	Jun 11, 2023	Few-Shot Learning	—Unverified
Are Fewer Labels Possible for Few-shot Learning?	Dec 10, 2020	ClusteringFew-Shot Learning	—Unverified
Are Few-shot Learning Benchmarks Too Simple ?	Sep 25, 2019	ClassificationFew-Shot Learning	—Unverified
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots	Jul 13, 2022	ChatbotFew-Shot Learning	—Unverified
Are Large Language Models Good Essay Graders?	Sep 19, 2024	Automated Essay ScoringFew-Shot Learning	—Unverified
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks	Jul 2, 2020	Few-Shot LearningLearning Theory	—Unverified
A Nested Bi-level Optimization Framework for Robust Few Shot Learning	Nov 13, 2020	Few-Shot LearningMeta-Learning	—Unverified
Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning	Oct 11, 2023	Argument MiningFew-Shot Learning	—Unverified
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification	Nov 16, 2021	Few-Shot LearningFew-Shot Text Classification	—Unverified
A Similarity Paradigm Through Textual Regularization Without Forgetting	Feb 20, 2025	Domain GeneralizationFew-Shot Learning	—Unverified
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class	Aug 12, 2024	ClassificationFew-Shot Image Classification	—Unverified
Supervised Graph Contrastive Learning for Few-shot Node Classification	Mar 29, 2022	ClassificationContrastive 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