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 1126–1150 of 2964 papers

Title	Date	Tasks	Status	Hype
Analogy-Forming Transformers for Few-Shot 3D Parsing	Apr 27, 2023	Few-Shot LearningMeta-Learning	—Unverified	0
Adaptive manifold for imbalanced transductive few-shot learning	Apr 27, 2023	Few-Shot Learning	—Unverified	0
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning	Apr 26, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Context-enriched molecule representations improve few-shot drug discovery	Apr 24, 2023	Drug DiscoveryFew-Shot Learning	CodeCode Available	1
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework	Apr 24, 2023	Few-Shot LearningMatrix Completion	—Unverified	0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection	Apr 24, 2023	Data AugmentationFew-Shot Learning	—Unverified	0
Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement	Apr 23, 2023	Few-Shot Learninggraph construction	CodeCode Available	1
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition	Apr 21, 2023	Few-Shot LearningGraph Matching	CodeCode Available	0
RPLKG: Robust Prompt Learning with Knowledge Graph	Apr 21, 2023	Domain GeneralizationFew-Shot Learning	—Unverified	0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups	Apr 21, 2023	ClassificationFew-Shot Learning	—Unverified	0
Clustered-patch Element Connection for Few-shot Learning	Apr 20, 2023	Few-Shot LearningFew-Shot Semantic Segmentation	CodeCode Available	0
MasakhaNEWS: News Topic Classification for African languages	Apr 19, 2023	ClassificationFew-Shot Learning	CodeCode Available	1
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning	Apr 19, 2023	Data AugmentationFew-Shot Learning	—Unverified	0
Few-shot Medical Image Segmentation via Cross-Reference Transformer	Apr 19, 2023	Few-Shot LearningImage Segmentation	—Unverified	0
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models	Apr 18, 2023	Few-Shot LearningPrediction	—Unverified	0
A Survey on Few-Shot Class-Incremental Learning	Apr 17, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation	Apr 15, 2023	Camouflaged Object SegmentationFew-shot Instance Segmentation	CodeCode Available	0
Instance-level Few-shot Learning with Class Hierarchy Mining	Apr 15, 2023	Few-shot Instance SegmentationFew-Shot Learning	CodeCode Available	0
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text	Apr 14, 2023	Few-Shot Learning	CodeCode Available	2
SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders	Apr 14, 2023	Few-Shot LearningPseudo Label	—Unverified	0
LSFSL: Leveraging Shape Information in Few-shot Learning	Apr 13, 2023	Few-Shot Learning	—Unverified	0
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence	Apr 13, 2023	Few-Shot LearningScene Classification	—Unverified	0
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning	Apr 13, 2023	Domain GeneralizationFew-Shot Learning	—Unverified	0
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning	Apr 12, 2023	Computational EfficiencyFew-Shot 3D Point Cloud Classification	CodeCode Available	1
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges	Apr 12, 2023	Autonomous DrivingFew-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