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 2551–2575 of 2964 papers

Title	Date	Tasks	Status	Hype
Meta-Meta Classification for One-Shot Learning	Apr 17, 2020	ClassificationFew-Shot Learning	CodeCode Available	0
Defining Benchmarks for Continual Few-Shot Learning	Apr 15, 2020	continual few-shot learningContinual Learning	CodeCode Available	1
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors	Apr 14, 2020	3D ReconstructionDecoder	CodeCode Available	1
Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL	Apr 13, 2020	Few-Shot LearningGeneral Classification	—Unverified	0
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation	Apr 13, 2020	Data AugmentationDiversity	CodeCode Available	1
Meta-Learning in Neural Networks: A Survey	Apr 11, 2020	Few-Shot LearningHyperparameter Optimization	CodeCode Available	1
MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning	Apr 10, 2020	Few-Shot Learning	CodeCode Available	0
From Generalized zero-shot learning to long-tail with class descriptors	Apr 5, 2020	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available	1
Knowledge Guided Metric Learning for Few-Shot Text Classification	Apr 4, 2020	ClassificationFew-Shot Learning	—Unverified	0
Optimization of Image Embeddings for Few Shot Learning	Apr 4, 2020	Few-Shot LearningGraph Neural Network	—Unverified	0
Learning to Segment the Tail	Apr 2, 2020	Few-Shot LearningIncremental Learning	CodeCode Available	1
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning	Apr 1, 2020	Data AugmentationFew-Shot Learning	—Unverified	0
Learning to Select Base Classes for Few-shot Classification	Apr 1, 2020	ClassificationFew-Shot Learning	—Unverified	0
FGN: Fully Guided Network for Few-Shot Instance Segmentation	Mar 31, 2020	Few-shot Instance SegmentationFew-Shot Learning	—Unverified	0
DPGN: Distribution Propagation Graph Network for Few-shot Learning	Mar 31, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Adversarial Feature Hallucination Networks for Few-Shot Learning	Mar 30, 2020	Data AugmentationDiversity	CodeCode Available	1
Improving out-of-distribution generalization via multi-task self-supervised pretraining	Mar 30, 2020	Adversarial RobustnessDomain Generalization	—Unverified	0
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining	Mar 29, 2020	Few-Shot LearningLanguage Modeling	CodeCode Available	0
Negative Margin Matters: Understanding Margin in Few-shot Classification	Mar 26, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
Instance Credibility Inference for Few-Shot Learning	Mar 26, 2020	Data AugmentationFew-Shot Image Classification	CodeCode Available	1
Learning What to Learn for Video Object Segmentation	Mar 25, 2020	Few-Shot LearningObject	CodeCode Available	1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?	Mar 25, 2020	AllFew-Shot Image Classification	CodeCode Available	1
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling	Mar 25, 2020	DiversityFew-Shot Learning	—Unverified	0
Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images	Mar 23, 2020	Few-Shot Learning	—Unverified	0
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification	Mar 20, 2020	feature selectionFew-Shot Image Classification	CodeCode Available	1

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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