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 2101–2125 of 2964 papers

Title	Date	Tasks	Status	Hype
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images	May 25, 2021	Few-Shot LearningInductive Bias	—Unverified	0
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift	May 25, 2021	Domain AdaptationFew-Shot Learning	CodeCode Available	1
Improving Few-shot Learning with Weakly-supervised Object Localization	May 25, 2021	Few-Shot LearningMetric Learning	—Unverified	0
True Few-Shot Learning with Language Models	May 24, 2021	Few-Shot LearningModel Selection	CodeCode Available	1
Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models	May 22, 2021	ClassificationFew-Shot Learning	—Unverified	0
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning	May 21, 2021	Cross-Lingual Word EmbeddingsFew-Shot Learning	—Unverified	0
Compositional Fine-Grained Low-Shot Learning	May 21, 2021	AttributeDiversity	—Unverified	0
Intriguing Properties of Vision Transformers	May 21, 2021	Few-Shot LearningSemantic Segmentation	CodeCode Available	1
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection	May 20, 2021	DiversityEvent Detection	—Unverified	0
Livewired Neural Networks: Making Neurons That Fire Together Wire Together	May 17, 2021	Few-Shot Learning	—Unverified	0
HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities	May 17, 2021	Few-Shot LearningMeta-Learning	—Unverified	0
Semi-supervised Contrastive Learning with Similarity Co-calibration	May 16, 2021	Contrastive LearningFew-Shot Learning	—Unverified	0
Ensemble Making Few-Shot Learning Stronger	May 12, 2021	Few-Shot LearningRelation	—Unverified	0
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning	May 12, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	0
Representation Learning via Global Temporal Alignment and Cycle-Consistency	May 11, 2021	Action ClassificationDynamic Time Warping	CodeCode Available	1
Few-Shot Learning by Integrating Spatial and Frequency Representation	May 11, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	1
Learning Implicit Temporal Alignment for Few-shot Video Classification	May 11, 2021	Action Recognition In VideosClassification	CodeCode Available	1
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study	May 8, 2021	Action Unit DetectionAttribute	—Unverified	0
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?	May 8, 2021	Dialogue State TrackingFew-Shot Learning	CodeCode Available	0
MetaKernel: Learning Variational Random Features with Limited Labels	May 8, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems	May 7, 2021	Few-Shot LearningTransfer Learning	—Unverified	0
Diffusion Mechanism in Residual Neural Network: Theory and Applications	May 7, 2021	Binary ClassificationClassification	CodeCode Available	1
Few-Shot Learning for Image Classification of Common Flora	May 7, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	0
Local descriptor-based multi-prototype network for few-shot Learning	May 5, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
Few-shot Partial Multi-view Learning	May 5, 2021	Few-Shot LearningMULTI-VIEW 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