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 2276–2300 of 2964 papers

Title	Date	Tasks	Status
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning	Oct 19, 2018	AttributeFew-Shot Learning	—Unverified
Zero- and Few-Shot NLP with Pretrained Language Models	May 1, 2022	Few-Shot Learning	—Unverified
Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer	May 30, 2022	Few-Shot LearningLung Cancer Diagnosis	—Unverified
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking	Jan 18, 2025	Binary ClassificationFact Checking	—Unverified
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning	Mar 5, 2024	Event Causality IdentificationFew-Shot Learning	—Unverified
Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review	Apr 29, 2020	Autonomous VehiclesCOVID-19 Diagnosis	—Unverified
Zero-Shot Learning via Class-Conditioned Deep Generative Models	Nov 15, 2017	Few-Shot LearningZero-Shot Learning	—Unverified
Learning to Select Base Classes for Few-shot Classification	Apr 1, 2020	ClassificationFew-Shot Learning	—Unverified
Learning to Skip for Language Modeling	Nov 26, 2023	Few-Shot LearningLanguage Modeling	—Unverified
Learn to Adapt to New Environment from Past Experience and Few Pilot	Sep 2, 2022	Deep LearningFew-Shot Learning	—Unverified
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning	Dec 7, 2022	Active LearningEfficient Exploration	—Unverified
Learn To Learn More Precisely	Aug 8, 2024	Few-Shot LearningMeta-Learning	—Unverified
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video	Jan 11, 2021	Few-Shot Learningimage-classification	—Unverified
Less is More: A Closer Look at Semantic-based Few-Shot Learning	Jan 10, 2024	Few-Shot LearningLanguage Modeling	—Unverified
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets	Sep 28, 2022	Computational EfficiencyDeblurring	—Unverified
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification	Jun 15, 2022	Few-Shot Learningimage-classification	—Unverified
Less than Few: Self-Shot Video Instance Segmentation	Apr 19, 2022	Few-Shot LearningInstance Segmentation	—Unverified
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning	Jan 18, 2024	Few-Shot LearningIn-Context Learning	—Unverified
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation	Aug 2, 2023	Data AugmentationFew-Shot Learning	—Unverified
Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation	Jul 24, 2023	Few-Shot LearningHate Speech Detection	—Unverified
Text Descriptions are Compressive and Invariant Representations for Visual Learning	Jul 10, 2023	DescriptiveFew-Shot Learning	—Unverified
Leveraging pre-trained language models for conversational information seeking from text	Mar 31, 2022	Few-Shot LearningIn-Context Learning	—Unverified
Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework	Oct 11, 2023	Few-Shot LearningSentiment Analysis	—Unverified
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs	Nov 5, 2024	Few-Shot LearningHallucination	—Unverified
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models	Mar 4, 2024	Few-Shot LearningHate Speech Detection	—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