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 1076–1100 of 2964 papers

Title	Date	Tasks	Status	Hype
Instance-based Max-margin for Practical Few-shot Recognition	May 27, 2023	Few-Shot Learning	—Unverified	0
On convex decision regions in deep network representations	May 26, 2023	Few-Shot Learning	CodeCode Available	0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation	May 26, 2023	Abstract Meaning RepresentationData Augmentation	CodeCode Available	0
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts	May 25, 2023	ClassificationFew-Shot Learning	CodeCode Available	0
Training on Thin Air: Improve Image Classification with Generated Data	May 24, 2023	Data AugmentationFew-Shot Learning	CodeCode Available	1
Sentiment Analysis in the Era of Large Language Models: A Reality Check	May 24, 2023	Aspect-Based Sentiment AnalysisFew-Shot Learning	CodeCode Available	1
A Survey of Diffusion Models in Natural Language Processing	May 24, 2023	Few-Shot LearningMachine Translation	—Unverified	0
LAraBench: Benchmarking Arabic AI with Large Language Models	May 24, 2023	BenchmarkingFew-Shot Learning	—Unverified	0
Improving few-shot learning-based protein engineering with evolutionary sampling	May 23, 2023	Few-Shot LearningProtein Design	CodeCode Available	1
Are Large Language Models Robust Coreference Resolvers?	May 23, 2023	coreference-resolutionCoreference Resolution	CodeCode Available	0
Do prompt positions really matter?	May 23, 2023	Few-Shot LearningNatural Language Understanding	CodeCode Available	0
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering	May 23, 2023	Fact VerificationFew-Shot Learning	—Unverified	0
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks	May 23, 2023	Few-Shot LearningLanguage Modeling	CodeCode Available	0
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning	May 23, 2023	Common Sense ReasoningCommon Sense Reasoning (Zero-Shot)	CodeCode Available	2
Active Learning Principles for In-Context Learning with Large Language Models	May 23, 2023	Active LearningDiversity	—Unverified	0
Improving Factuality and Reasoning in Language Models through Multiagent Debate	May 23, 2023	Few-Shot LearningLanguage Modeling	CodeCode Available	2
A Rational Model of Dimension-reduced Human Categorization	May 22, 2023	Few-Shot Learningmodel	—Unverified	0
Small Language Models Improve Giants by Rewriting Their Outputs	May 22, 2023	Few-Shot LearningIn-Context Learning	CodeCode Available	1
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?	May 22, 2023	Data-free Knowledge DistillationFew-Shot Learning	CodeCode Available	1
Automated Few-shot Classification with Instruction-Finetuned Language Models	May 21, 2023	ClassificationFew-Shot Learning	CodeCode Available	0
PointGPT: Auto-regressively Generative Pre-training from Point Clouds	May 19, 2023	3D Point Cloud ClassificationDecoder	CodeCode Available	2
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment	May 19, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment	May 19, 2023	Few-Shot Learning	CodeCode Available	1
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes	May 18, 2023	ClusteringFew-Shot Learning	—Unverified	0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection	May 18, 2023	Anomaly DetectionFew-Shot Learning	CodeCode Available	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