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 926–950 of 2964 papers

Title	Date	Tasks	Status
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot	Jun 1, 2020	Few-Shot LearningTransfer Learning	—Unverified
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging	May 2, 2025	BenchmarkingComputational Efficiency	—Unverified
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners	Jan 1, 2024	Adversarial RobustnessFew-Shot Learning	—Unverified
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules	Aug 26, 2024	ClassificationFew-Shot Image Classification	—Unverified
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches	Nov 25, 2024	Data AugmentationDrug Discovery	—Unverified
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation	Jul 29, 2024	Code TranslationFew-Shot Learning	—Unverified
Can Explanations Be Useful for Calibrating Black Box Models?	Nov 16, 2021	Extractive Question-AnsweringFew-Shot Learning	—Unverified
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification	Nov 6, 2023	Data AugmentationFew-Shot Learning	—Unverified
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping	Feb 28, 2023	3D Point Cloud ClassificationContrastive Learning	—Unverified
Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability	Nov 29, 2024	Domain Adaptationdomain classification	—Unverified
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions	May 13, 2025	Federated LearningFew-Shot Learning	—Unverified
Annotation-Efficient Untrimmed Video Action Recognition	Nov 30, 2020	Action RecognitionContrastive Learning	—Unverified
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines	May 8, 2025	Contrastive LearningFew-Shot Learning	—Unverified
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance	Nov 9, 2020	Few-Shot LearningIntrusion Detection	—Unverified
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning	Dec 12, 2021	Anomaly DetectionCross-Domain Few-Shot	—Unverified
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios	May 17, 2024	Active LearningFew-Shot Learning	—Unverified
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network	May 1, 2022	Cross-Lingual Word EmbeddingsEmotion Classification	—Unverified
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models	Apr 18, 2023	Few-Shot LearningPrediction	—Unverified
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM	May 10, 2024	ArticlesFew-Shot Learning	—Unverified
Enabling the Network to Surf the Internet	Feb 24, 2021	Few-Shot LearningSelf-Supervised Learning	—Unverified
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks	Dec 16, 2022	Federated LearningFew-Shot Learning	—Unverified
Enabling ISP-less Low-Power Computer Vision	Oct 11, 2022	DemosaickingFew-Shot Learning	—Unverified
Few-Shot Learning Through an Information Retrieval Lens	Jul 9, 2017	Few-Shot LearningGeneral Classification	—Unverified
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification	Nov 16, 2021	Few-Shot LearningFew-Shot Text Classification	—Unverified
Few-shot learning using pre-training and shots, enriched by pre-trained samples	Sep 19, 2020	Few-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