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	Hype
A MIMO Radar-Based Metric Learning Approach for Activity Recognition	Nov 2, 2021	Activity RecognitionFew-Shot Learning	—Unverified	0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP	Nov 2, 2021	DiversityFew-Shot Learning	—Unverified	0
Few-Shot Named Entity Recognition: An Empirical Baseline Study	Nov 1, 2021	Few-Shot LearningMeta-Learning	—Unverified	0
Learning Prototype Representations Across Few-Shot Tasks for Event Detection	Nov 1, 2021	Event DetectionFew-Shot Learning	CodeCode Available	0
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification	Nov 1, 2021	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	1
Towards Realistic Few-Shot Relation Extraction	Nov 1, 2021	ClassificationFew-Shot Learning	CodeCode Available	1
Continual Few-Shot Learning for Text Classification	Nov 1, 2021	Classificationcontinual few-shot learning	CodeCode Available	0
Data-Efficient Language Shaped Few-shot Image Classification	Nov 1, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study	Nov 1, 2021	Domain AdaptationFew-Shot Learning	—Unverified	0
Few-shot learning with improved local representations via bias rectify module	Nov 1, 2021	Few-Shot LearningMetric Learning	—Unverified	0
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima	Oct 30, 2021	Few-Shot Class-Incremental LearningFew-Shot Learning	CodeCode Available	1
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning	Oct 30, 2021	Few-Shot LearningFew-Shot Semantic Segmentation	—Unverified	0
MetaICL: Learning to Learn In Context	Oct 29, 2021	Few-Shot LearningIn-Context Learning	CodeCode Available	1
Domain Agnostic Few-Shot Learning For Document Intelligence	Oct 29, 2021	ClassificationCross-Domain Few-Shot	—Unverified	0
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose	Oct 27, 2021	3D Pose EstimationFew-Shot Learning	CodeCode Available	1
On sensitivity of meta-learning to support data	Oct 26, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Non-Gaussian Gaussian Processes for Few-Shot Regression	Oct 26, 2021	Few-Shot LearningGaussian Processes	CodeCode Available	1
Self-Denoising Neural Networks for Few Shot Learning	Oct 26, 2021	Action DetectionDenoising	—Unverified	0
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware	Oct 26, 2021	Few-Shot LearningMalware Detection	—Unverified	0
Meta-Learning for Multi-Label Few-Shot Classification	Oct 26, 2021	ClassificationFew-Shot Learning	—Unverified	0
Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference	Oct 26, 2021	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning	Oct 25, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation	Oct 23, 2021	Few-Shot LearningFew-Shot Semantic Segmentation	CodeCode Available	1
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation	Oct 23, 2021	Few-Shot LearningOut-of-Distribution Generalization	CodeCode Available	1
GCCN: Global Context Convolutional Network	Oct 22, 2021	Few-Shot Image ClassificationFew-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