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 2176–2200 of 2964 papers

Title	Date	Tasks	Status	Hype
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data	Mar 25, 2021	Autonomous DrivingFew-Shot Learning	—Unverified	0
Learning Dynamic Alignment via Meta-filter for Few-shot Learning	Mar 25, 2021	Few-Shot LearningPosition	CodeCode Available	1
Orthogonal Projection Loss	Mar 25, 2021	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
Detecting Hate Speech with GPT-3	Mar 23, 2021	Few-Shot LearningHate Speech Detection	CodeCode Available	1
Prototypical Representation Learning for Relation Extraction	Mar 22, 2021	Few-Shot LearningRelation	CodeCode Available	1
Improving and Simplifying Pattern Exploiting Training	Mar 22, 2021	Few-Shot Learning	CodeCode Available	1
Multi-level Metric Learning for Few-shot Image Recognition	Mar 21, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Real-Time Visual Object Tracking via Few-Shot Learning	Mar 18, 2021	Few-Shot LearningObject	—Unverified	0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State	Mar 17, 2021	Brain Computer InterfaceClassification	—Unverified	0
Towards Few-Shot Fact-Checking via Perplexity	Mar 17, 2021	Fact CheckingFew-Shot Learning	—Unverified	0
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning	Mar 16, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	0
DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network	Mar 15, 2021	Few-Shot Learning	—Unverified	0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video	Mar 15, 2021	Anomaly DetectionFew-Shot Learning	—Unverified	0
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors	Mar 13, 2021	Few-Shot LearningMRI Reconstruction	—Unverified	0
Inductive Relation Prediction by BERT	Mar 12, 2021	Few-Shot LearningInductive Learning	CodeCode Available	1
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding	Mar 10, 2021	Contrastive LearningCross-Domain Few-Shot Object Detection	CodeCode Available	1
Multi-Pretext Attention Network for Few-shot Learning with Self-supervision	Mar 10, 2021	ClusteringFew-Shot Learning	CodeCode Available	0
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data	Mar 8, 2021	DecoderDisentanglement	—Unverified	0
Few-shot Learning for Slot Tagging with Attentive Relational Network	Mar 3, 2021	Few-Shot Learning	—Unverified	0
Few-shot Open-set Recognition by Transformation Consistency	Mar 2, 2021	Few-Shot LearningOpen Set Learning	CodeCode Available	1
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics	Mar 2, 2021	Few-Shot LearningProgram Synthesis	—Unverified	0
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning	Mar 1, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
OmniNet: Omnidirectional Representations from Transformers	Mar 1, 2021	de-enFew-Shot Learning	CodeCode Available	0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection	Mar 1, 2021	Anomaly DetectionFew-Shot Learning	—Unverified	0
Domain Adaptation for Learning Generator from Paired Few-Shot Data	Feb 25, 2021	DiversityDomain Adaptation	—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