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 1226–1250 of 2964 papers

Title	Date	Tasks	Status
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?	May 21, 2024	Decision MakingDisease Prediction	—Unverified
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning	Mar 7, 2024	Continual LearningDefinition Extraction	—Unverified
Bayesian Embeddings for Few-Shot Open World Recognition	Jul 29, 2021	Decision MakingFew-Shot Learning	—Unverified
Deep few-shot learning for bi-temporal building change detection	Aug 25, 2021	Change DetectionFew-Shot Learning	—Unverified
Batch Group Normalization	Dec 4, 2020	Domain AdaptationFew-Shot Learning	—Unverified
A metric learning approach for endoscopic kidney stone identification	Jul 13, 2023	Few-Shot LearningKnowledge Distillation	—Unverified
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition	Jun 20, 2020	ClusteringEnsemble Learning	—Unverified
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation	Dec 24, 2024	Comment GenerationFew-Shot Learning	—Unverified
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models	Oct 1, 2019	Few-Shot LearningForm	—Unverified
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization	Apr 20, 2025	Compressive SensingFew-Shot Learning	—Unverified
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems	May 6, 2025	Few-Shot LearningQuantum Machine Learning	—Unverified
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes	May 18, 2023	ClusteringFew-Shot Learning	—Unverified
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models	Feb 19, 2024	Code RepairFew-Shot Learning	—Unverified
Decomposed Prototype Learning for Few-Shot Scene Graph Generation	Mar 20, 2023	Few-Shot LearningGraph Generation	—Unverified
Baby steps towards few-shot learning with multiple semantics	Jun 5, 2019	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions	Mar 24, 2025	Few-Shot Learning	—Unverified
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing	Aug 25, 2024	Data AugmentationDiversity	—Unverified
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs	Aug 2, 2024	Computational EfficiencyDrug Design	—Unverified
DCP: Learning Accelerator Dataflow for Neural Network via Propagation	Oct 9, 2024	Few-Shot LearningScheduling	—Unverified
Dataset Bias Prediction for Few-Shot Image Classification	Sep 29, 2021	ClassificationFew-Shot Image Classification	—Unverified
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment	May 19, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
High-level semantic feature matters few-shot unsupervised domain adaptation	Jan 5, 2023	Domain AdaptationFew-Shot Learning	—Unverified
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?	Feb 14, 2024	Autonomous DrivingFew-Shot Learning	—Unverified
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class	Dec 19, 2022	Document ClassificationFew-Shot Learning	—Unverified
Dataset Bias in Few-shot Image Recognition	Aug 18, 2020	DiversityFew-Shot 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