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 2476–2500 of 2964 papers

Title	Date	Tasks	Status
High-dimensional separability for one- and few-shot learning	Jun 28, 2021	Domain AdaptationFew-Shot Learning	—Unverified
High-level semantic feature matters few-shot unsupervised domain adaptation	Jan 5, 2023	Domain AdaptationFew-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
How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach	Dec 16, 2024	Few-Shot LearningKnowledge Graphs	—Unverified
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers	Mar 4, 2024	Few-Shot LearningLanguage Modeling	—Unverified
How Fine-Tuning Allows for Effective Meta-Learning	May 5, 2021	Few-Shot LearningMeta-Learning	—Unverified
How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?	Feb 21, 2024	Few-Shot LearningRelation	—Unverified
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?	May 21, 2024	Decision MakingDisease Prediction	—Unverified
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?	Feb 14, 2024	Autonomous DrivingFew-Shot Learning	—Unverified
How to fine-tune deep neural networks in few-shot learning?	Dec 1, 2020	Deep LearningFew-Shot Learning	—Unverified
How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models	Sep 3, 2022	Few-Shot Learning	—Unverified
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?	Feb 18, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme	Jun 26, 2024	Anomaly DetectionAnomaly Segmentation	—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
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning	Nov 19, 2020	Data AugmentationFew-Shot Learning	—Unverified
Hybrid Graph Neural Networks for Few-Shot Learning	Dec 13, 2021	Few-Shot Learning	—Unverified
Epistemic Graph: A Plug-And-Play Module For Hybrid Representation Learning	May 30, 2023	Few-Shot LearningKnowledge Graphs	—Unverified
Hyperbolic Dual Feature Augmentation for Open-Environment	Jun 10, 2025	class-incremental learningClass Incremental Learning	—Unverified
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation	Jan 1, 2025	class-incremental learningClass Incremental Learning	—Unverified
Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin	Sep 18, 2023	Few-Shot LearningRepresentation Learning	—Unverified
Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data	Nov 17, 2024	Classification Of Hyperspectral ImagesFew-Shot Learning	—Unverified
Hyperspherical embedding for novel class classification	Feb 5, 2021	ClassificationFew-Shot Learning	—Unverified
HyperTransformer: Attention-Based CNN Model Generation from Few Samples	Sep 29, 2021	Few-Shot Learning	—Unverified
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought	Jun 20, 2024	Action AnticipationContinual 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