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 1851–1875 of 2964 papers

Title	Date	Tasks	Status
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models	May 4, 2024	Few-Shot LearningText to SQL	—Unverified
OpineSum: Entailment-based self-training for abstractive opinion summarization	Dec 21, 2022	Abstractive Text SummarizationArticles	—Unverified
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation	Jul 4, 2023	Active LearningBinary Classification	—Unverified
Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain	Nov 20, 2023	Few-Shot Learningnamed-entity-recognition	—Unverified
Optimization of Image Embeddings for Few Shot Learning	Apr 4, 2020	Few-Shot LearningGraph Neural Network	—Unverified
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages	Mar 14, 2025	Binary ClassificationFew-Shot Learning	—Unverified
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning	Oct 18, 2021	Data AugmentationDiversity	—Unverified
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning	Apr 13, 2023	Domain GeneralizationFew-Shot Learning	—Unverified
Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation	Jun 2, 2025	Few-Shot LearningImage Segmentation	—Unverified
PAC-Bayes meta-learning with implicit task-specific posteriors	Mar 5, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent	Oct 26, 2023	Data AugmentationFew-Shot Learning	—Unverified
PALM: Few-Shot Prompt Learning for Audio Language Models	Sep 29, 2024	Few-Shot LearningPrompt Learning	—Unverified
Pareto Self-Supervised Training for Few-Shot Learning	Apr 16, 2021	Auxiliary LearningFew-Shot Learning	—Unverified
PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks	Mar 18, 2024	Continual LearningFew-Shot Learning	—Unverified
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning	Feb 8, 2021	AllFew-Shot Learning	—Unverified
Partitioning Image Representation in Contrastive Learning	Mar 20, 2022	Contrastive LearningData Augmentation	—Unverified
Partner-Assisted Learning for Few-Shot Image Classification	Sep 15, 2021	ClassificationFew-Shot Image Classification	—Unverified
PatchMix Augmentation to Identify Causal Features in Few-shot Learning	Nov 29, 2022	Data AugmentationFew-Shot Learning	—Unverified
PatchProto Networks for Few-shot Visual Anomaly Classification	Oct 7, 2023	Anomaly ClassificationClassification	—Unverified
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition	May 26, 2020	Few-Shot LearningHandwriting Recognition	—Unverified
PeerArg: Argumentative Peer Review with LLMs	Sep 25, 2024	Decision MakingFew-Shot Learning	—Unverified
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC	Apr 30, 2024	Few-Shot Learning	—Unverified
PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems	May 7, 2021	Few-Shot LearningTransfer Learning	—Unverified
PennSyn2Real: Training Object Recognition Models without Human Labeling	Sep 22, 2020	4kFew-Shot Learning	—Unverified
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction	Dec 22, 2020	Few-Shot LearningMeta-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