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

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Showing 626–650 of 2964 papers

Title	Date	Tasks	Status	Hype
Graph Meta Learning via Local Subgraphs	Jun 14, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network	Jun 10, 2020	Few-Shot LearningGeneral Classification	CodeCode Available	1
Multi-view Contrastive Learning for Online Knowledge Distillation	Jun 7, 2020	ClassificationContrastive Learning	CodeCode Available	1
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	Jun 6, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Interpretable Time-series Classification on Few-shot Samples	Jun 3, 2020	ClassificationFew-Shot Learning	CodeCode Available	1
Adaptive Subspaces for Few-Shot Learning	Jun 1, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Attentive Weights Generation for Few Shot Learning via Information Maximization	Jun 1, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations	May 18, 2020	8kFew-Shot Learning	CodeCode Available	1
Boosting on the shoulders of giants in quantum device calibration	May 13, 2020	BIG-bench Machine LearningFew-Shot Learning	CodeCode Available	1
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching	May 11, 2020	End-To-End Dialogue ModellingFew-Shot Learning	CodeCode Available	1
Harvesting and Refining Question-Answer Pairs for Unsupervised QA	May 6, 2020	Few-Shot LearningQuestion Answering	CodeCode Available	1
DenoiSeg: Joint Denoising and Segmentation	May 6, 2020	DenoisingFew-Shot Learning	CodeCode Available	1
Towards Fast Adaptation of Neural Architectures with Meta Learning	May 1, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Few-Shot Learning for Opinion Summarization	Apr 30, 2020	Few-Shot LearningInformativeness	CodeCode Available	1
Physarum Powered Differentiable Linear Programming Layers and Applications	Apr 30, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation	Apr 29, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation	Apr 27, 2020	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol	Apr 23, 2020	Domain AdaptationFew-Shot Learning	CodeCode Available	1
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
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
From Generalized zero-shot learning to long-tail with class descriptors	Apr 5, 2020	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available	1
Learning to Segment the Tail	Apr 2, 2020	Few-Shot LearningIncremental Learning	CodeCode Available	1
DPGN: Distribution Propagation Graph Network for Few-shot Learning	Mar 31, 2020	Few-Shot LearningMeta-Learning	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