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 2001–2025 of 2964 papers

Title	Date	Tasks	Status
A Framework of Meta Functional Learning for Regularising Knowledge Transfer	Mar 28, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography	Mar 28, 2022	Few-Shot LearningGesture Recognition	—Unverified
Compare learning: bi-attention network for few-shot learning	Mar 25, 2022	Few-Shot LearningMetric Learning	—Unverified
Multidimensional Belief Quantification for Label-Efficient Meta-Learning	Mar 23, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Partitioning Image Representation in Contrastive Learning	Mar 20, 2022	Contrastive LearningData Augmentation	—Unverified
Incremental Few-Shot Learning via Implanting and Compressing	Mar 19, 2022	Few-Shot Learningimage-classification	—Unverified
Few-Shot Learning on Graphs	Mar 17, 2022	Few-Shot LearningGraph Mining	—Unverified
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications	Mar 16, 2022	AutoMLFew-Shot Learning	—Unverified
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning	Mar 15, 2022	ClassificationFew-Shot Learning	—Unverified
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning	Mar 15, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition	Mar 14, 2022	Activity RecognitionFew-Shot Learning	—Unverified
Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning	Mar 9, 2022	Few-Shot LearningMeta-Learning	CodeCode Available
Automated Few-Shot Time Series Forecasting based on Bi-level Programming	Mar 7, 2022	Decision MakingFew-Shot Learning	—Unverified
Pre-trained Token-replaced Detection Model as Few-shot Learner	Mar 7, 2022	Few-Shot LearningSentence	CodeCode Available
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning	Mar 7, 2022	Few-Shot LearningMeta-Learning	—Unverified
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification	Mar 4, 2022	counterfactualFew-Shot Learning	CodeCode Available
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning	Mar 3, 2022	Few-Shot LearningMeta-Learning	—Unverified
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning	Mar 3, 2022	Domain AdaptationFew-Shot Learning	—Unverified
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models	Mar 3, 2022	Few-Shot LearningRepresentation Learning	—Unverified
Interpretable Concept-based Prototypical Networks for Few-Shot Learning	Feb 27, 2022	Few-Shot LearningMeta-Learning	—Unverified
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation	Feb 27, 2022	Data AugmentationDisentanglement	CodeCode Available
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
Semantically Proportional Patchmix for Few-Shot Learning	Feb 17, 2022	Few-Shot LearningTransfer Learning	—Unverified
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization	Feb 15, 2022	Event DetectionEvent Extraction	—Unverified
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation	Feb 14, 2022	Few-Shot LearningSegmentation	—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