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 651–675 of 2964 papers

Title	Date	Tasks	Status	Hype
Adversarial Feature Hallucination Networks for Few-Shot Learning	Mar 30, 2020	Data AugmentationDiversity	CodeCode Available	1
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
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification	Mar 20, 2020	feature selectionFew-Shot Image Classification	CodeCode Available	1
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning	Mar 19, 2020	Few-Shot LearningNovel Concepts	CodeCode Available	1
Domain-Adaptive Few-Shot Learning	Mar 19, 2020	Domain AdaptationFew-Shot Learning	CodeCode Available	1
Context-Transformer: Tackling Object Confusion for Few-Shot Detection	Mar 16, 2020	DiversityFew-Shot Learning	CodeCode Available	1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning	Mar 15, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification	Mar 14, 2020	DiversityFew-Shot Learning	CodeCode Available	1
Meta-Learning Initializations for Low-Resource Drug Discovery	Mar 12, 2020	Drug DiscoveryFew-Shot Learning	CodeCode Available	1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning	Mar 9, 2020	Few-Shot LearningGeneral Classification	CodeCode Available	1
On the Texture Bias for Few-Shot CNN Segmentation	Mar 9, 2020	Few-Shot LearningFew-Shot Semantic Segmentation	CodeCode Available	1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study	Mar 6, 2020	Few-Shot LearningModel Selection	CodeCode Available	1
Few-shot Natural Language Generation for Task-Oriented Dialog	Feb 27, 2020	Data-to-Text GenerationFew-Shot Learning	CodeCode Available	1
Meta-Learned Confidence for Few-shot Learning	Feb 27, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures	Feb 27, 2020	Active LearningClassification	CodeCode Available	1
Evaluating Weakly Supervised Object Localization Methods Right	Jan 21, 2020	Few-Shot LearningModel Selection	CodeCode Available	1
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning	Jan 12, 2020	Few-Shot LearningRelation	CodeCode Available	1
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network	Jan 5, 2020	Face SwappingFew-Shot Learning	CodeCode Available	1
A Broader Study of Cross-Domain Few-Shot Learning	Dec 16, 2019	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Meta-Learning of Neural Architectures for Few-Shot Learning	Nov 25, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	1
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning	Nov 12, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Penalty Method for Inversion-Free Deep Bilevel Optimization	Nov 8, 2019	Bilevel OptimizationData Poisoning	CodeCode Available	1

Show:10 25 50

← PrevPage 27 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