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 2226–2250 of 2964 papers

Title	Date	Tasks	Status	Hype
Neural Data Augmentation via Example Extrapolation	Feb 2, 2021	Data AugmentationDiversity	CodeCode Available	0
Few-shot Learning for CT Scan based COVID-19 Diagnosis	Feb 1, 2021	Computed Tomography (CT)COVID-19 Diagnosis	—Unverified	0
Few-shot Image Classification with Multi-Facet Prototypes	Feb 1, 2021	ClassificationFew-Shot Image Classification	—Unverified	0
Few-Shot Learning for Road Object Detection	Jan 29, 2021	Few-Shot LearningFew-Shot Object Detection	—Unverified	0
Generalising via Meta-Examples for Continual Learning in the Wild	Jan 28, 2021	Continual LearningFew-Shot Learning	CodeCode Available	1
Similarity of Classification Tasks	Jan 27, 2021	ClassificationFew-Shot Learning	CodeCode Available	0
Edge-Labeling based Directed Gated Graph Network for Few-shot Learning	Jan 27, 2021	Few-Shot Learning	CodeCode Available	0
Combat Data Shift in Few-shot Learning with Knowledge Graph	Jan 27, 2021	Few-Shot LearningMeta-Learning	—Unverified	0
Supervised Momentum Contrastive Learning for Few-Shot Classification	Jan 26, 2021	ClassificationContrastive Learning	—Unverified	0
Few-Shot Semantic Parsing for New Predicates	Jan 26, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	1
TLRM: Task-level Relation Module for GNN-based Few-Shot Learning	Jan 25, 2021	Few-Shot LearningRelation	—Unverified	0
Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks	Jan 24, 2021	Few-Shot LearningRepresentation Learning	CodeCode Available	1
Learn from Concepts: Towards the Purified Memory for Few-shot Learning	Jan 23, 2021	Few-Shot LearningGraph Neural Network	—Unverified	0
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning	Jan 23, 2021	Few-Shot LearningMeta-Learning	—Unverified	0
Stress Testing of Meta-learning Approaches for Few-shot Learning	Jan 21, 2021	Few-Shot LearningMeta-Learning	—Unverified	0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates	Jan 19, 2021	Bayesian OptimizationFew-Shot Learning	—Unverified	0
Cross-domain few-shot learning with unlabelled data	Jan 19, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Machine learning with limited data	Jan 18, 2021	BIG-bench Machine LearningCross-Domain Few-Shot	—Unverified	0
What Makes Good In-Context Examples for GPT-3?	Jan 17, 2021	Few-Shot LearningNatural Language Understanding	CodeCode Available	4
Free Lunch for Few-shot Learning: Distribution Calibration	Jan 16, 2021	Few-Shot Learning	CodeCode Available	1
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition	Jan 13, 2021	Few-Shot LearningMeta-Learning	—Unverified	0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video	Jan 11, 2021	Few-Shot Learningimage-classification	—Unverified	0
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition	Jan 8, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Few-Shot Learning with Class Imbalance	Jan 7, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	1
Local Propagation for Few-Shot Learning	Jan 5, 2021	Few-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