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 701–725 of 2964 papers

Title	Date	Tasks	Status	Hype
Neural Fine-Tuning Search for Few-Shot Learning	Jun 15, 2023	Few-Shot LearningNeural Architecture Search	CodeCode Available	1
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification	Jun 30, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners	Oct 12, 2021	Few-Shot LearningIn-Context Learning	CodeCode Available	1
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models	Apr 25, 2024	Data AugmentationDomain Generalization	CodeCode Available	1
A Study of Few-Shot Audio Classification	Dec 2, 2020	Audio ClassificationBIG-bench Machine Learning	—Unverified	0
Exploring the Space of Key-Value-Query Models with Intention	May 17, 2023	Deep LearningFew-Shot Learning	—Unverified	0
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs	Jan 23, 2025	DiagnosticFew-Shot Learning	—Unverified	0
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics	Mar 2, 2021	Few-Shot LearningProgram Synthesis	—Unverified	0
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data	Mar 27, 2025	Few-Shot LearningMeta-Learning	—Unverified	0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation	Dec 28, 2020	Few-Shot LearningPoint Cloud Segmentation	—Unverified	0
Compositional Generalization via Neural-Symbolic Stack Machines	Aug 15, 2020	Few-Shot LearningMachine Translation	—Unverified	0
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning	Oct 21, 2021	Few-Shot Learning	—Unverified	0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning	Oct 14, 2024	Few-Shot LearningHyperparameter Optimization	—Unverified	0
Compositional Fine-Grained Low-Shot Learning	May 21, 2021	AttributeDiversity	—Unverified	0
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks	Oct 26, 2020	Few-Shot LearningMetric Learning	—Unverified	0
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models	Jun 15, 2023	Electrical EngineeringFew-Shot Learning	—Unverified	0
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery	Jul 22, 2021	Few-Shot LearningZero-Shot Learning	—Unverified	0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing	May 12, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
AgileNet: Lightweight Dictionary-based Few-shot Learning	May 21, 2018	Few-Shot Learning	—Unverified	0
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding	Apr 2, 2025	Activity RecognitionFew-Shot Learning	—Unverified	0
Composing Diffusion Policies for Few-shot Learning of Movement Trajectories	Oct 22, 2024	Few-Shot Learning	—Unverified	0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation	Feb 26, 2019	Data AugmentationFew-Shot Learning	—Unverified	0
Agile gesture recognition for low-power applications: customisation for generalisation	Mar 12, 2024	Few-Shot LearningGesture Recognition	—Unverified	0
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases	Mar 25, 2024	Few-Shot Learning	—Unverified	0
Associative Adversarial Learning Based on Selective Attack	Dec 28, 2021	Adversarial RobustnessFew-Shot Learning	—Unverified	0

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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