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 851–875 of 2964 papers

Title	Date	Tasks	Status
Few-Shot Knowledge Graph Completion with Data Fusion and Augmentation	Nov 16, 2021	Few-Shot LearningKnowledge Graph Completion	—Unverified
Episodic-free Task Selection for Few-shot Learning	Jan 31, 2024	Contrastive LearningFew-Shot Learning	—Unverified
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings	Mar 11, 2024	Few-Shot Learningobject-detection	—Unverified
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?	Jul 6, 2023	Few-Shot LearningModel Selection	—Unverified
CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications	Jul 18, 2024	Few-Shot Learning	—Unverified
A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications	Dec 3, 2024	Few-Shot Learning	—Unverified
EPIC: Efficient Position-Independent Caching for Serving Large Language Models	Oct 20, 2024	ChunkingFew-Shot Learning	—Unverified
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets	Aug 4, 2024	Few-Shot LearningMachine Reading Comprehension	—Unverified
Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning	Aug 22, 2024	Few-Shot Learning	—Unverified
Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model	Mar 8, 2025	Few-Shot LearningLanguage Modeling	—Unverified
Few-shot Intent Classification and Slot Filling with Retrieved Examples	Apr 12, 2021	ClassificationFew-Shot Learning	—Unverified
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition	Sep 24, 2024	Domain AdaptationEEG	—Unverified
Few-shot learning approaches for classifying low resource domain specific software requirements	Feb 14, 2023	Few-Shot Learning	—Unverified
Evolutionary Pre-Prompt Optimization for Mathematical Reasoning	Dec 5, 2024	Few-Shot LearningGSM8K	—Unverified
Causal Interventions-based Few-Shot Named Entity Recognition	May 3, 2023	Few-Shot Learningfew-shot-ner	—Unverified
Evolving Losses for Unsupervised Video Representation Learning	Feb 26, 2020	Action RecognitionFew-Shot Learning	—Unverified
Ensemble Making Few-Shot Learning Stronger	May 12, 2021	Few-Shot LearningRelation	—Unverified
Few-shot Image Recognition with Manifolds	Oct 22, 2020	Few-Shot LearningPrivacy Preserving	—Unverified
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning	Feb 5, 2022	class-incremental learningClass Incremental Learning	—Unverified
Catalysis distillation neural network for the few shot open catalyst challenge	May 31, 2023	Computational chemistryFew-Shot Learning	—Unverified
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning	Oct 18, 2023	Compressive SensingFew-Shot Learning	—Unverified
ExpertGenQA: Open-ended QA generation in Specialized Domains	Mar 4, 2025	DiversityFew-Shot Learning	—Unverified
Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning	Oct 11, 2023	Deep Reinforcement LearningFew-Shot Learning	—Unverified
Explaining CLIP's performance disparities on data from blind/low vision users	Nov 29, 2023	Few-Shot Learningzero-shot-classification	—Unverified
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs	Jun 4, 2024	BenchmarkingFairness	—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