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 2626–2650 of 2964 papers

Title	Date	Tasks	Status
Few-Shot Learning with Geometric Constraints	Mar 20, 2020	Few-Shot Learning	—Unverified
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients	Mar 18, 2020	Few-Shot LearningFood Recognition	—Unverified
Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification	Mar 18, 2020	ClassificationClustering	—Unverified
CAFENet: Class-Agnostic Few-Shot Edge Detection Network	Mar 18, 2020	DecoderEdge Detection	—Unverified
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification	Mar 18, 2020	ClassificationFew-Shot Learning	—Unverified
Semi-supervised few-shot learning for medical image segmentation	Mar 18, 2020	Few-Shot LearningImage Segmentation	—Unverified
StarNet: towards Weakly Supervised Few-Shot Object Detection	Mar 15, 2020	ClassificationFew-Shot Learning	CodeCode Available
Incremental Few-Shot Object Detection	Mar 10, 2020	Few-Shot LearningFew-Shot Object Detection	—Unverified
PAC-Bayes meta-learning with implicit task-specific posteriors	Mar 5, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error	Mar 4, 2020	Few-Shot LearningMeta-Learning	—Unverified
Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning	Mar 2, 2020	ClassificationFew-Shot Learning	—Unverified
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning	Feb 29, 2020	Few-Shot LearningMetric Learning	—Unverified
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning	Feb 28, 2020	DenoisingFew-Shot Learning	—Unverified
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?	Feb 27, 2020	Few-Shot LearningMeta-Learning	—Unverified
Evolving Losses for Unsupervised Video Representation Learning	Feb 26, 2020	Action RecognitionFew-Shot Learning	—Unverified
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments	Feb 26, 2020	Face RecognitionFew-Shot Learning	CodeCode Available
The Sample Complexity of Meta Sparse Regression	Feb 22, 2020	Few-Shot LearningMeta-Learning	—Unverified
Modelling Latent Skills for Multitask Language Generation	Feb 21, 2020	Few-Shot LearningText Generation	—Unverified
Few-shot acoustic event detection via meta-learning	Feb 21, 2020	Event DetectionFew-Shot Learning	—Unverified
Few-Shot Learning via Learning the Representation, Provably	Feb 21, 2020	Few-Shot LearningRepresentation Learning	—Unverified
Structured Prediction for Conditional Meta-Learning	Feb 20, 2020	Few-Shot LearningMeta-Learning	CodeCode Available
Few-Shot Few-Shot Learning and the role of Spatial Attention	Feb 18, 2020	Few-Shot LearningMeta-Learning	—Unverified
Exploiting the Matching Information in the Support Set for Few Shot Event Classification	Feb 13, 2020	ClassificationFew-Shot Learning	—Unverified
Meta-Learning across Meta-Tasks for Few-Shot Learning	Feb 11, 2020	Domain AdaptationFew-Shot Learning	—Unverified
Task Augmentation by Rotating for Meta-Learning	Feb 8, 2020	Data AugmentationFew-Shot Learning	CodeCode Available

Show:10 25 50

← PrevPage 106 of 119Next →

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