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 2501–2525 of 2964 papers

Title	Date	Tasks	Status	Hype
Brain-inspired global-local learning incorporated with neuromorphic computing	Jun 5, 2020	Continual LearningFew-Shot Learning	—Unverified	0
Interpretable Time-series Classification on Few-shot Samples	Jun 3, 2020	ClassificationFew-Shot Learning	CodeCode Available	1
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot	Jun 1, 2020	Few-Shot LearningTransfer Learning	—Unverified	0
Few-Shot Pill Recognition	Jun 1, 2020	Few-Shot Learning	—Unverified	0
METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos	Jun 1, 2020	Few-Shot LearningRelation Network	—Unverified	0
Adaptive Subspaces for Few-Shot Learning	Jun 1, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition	Jun 1, 2020	Few-Shot LearningLong-tail Learning	—Unverified	0
Attentive Weights Generation for Few Shot Learning via Information Maximization	Jun 1, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation	Jun 1, 2020	Few-Shot LearningMORPH	—Unverified	0
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data	Jun 1, 2020	ClassificationClustering	—Unverified	0
Towards Context-Agnostic Learning Using Synthetic Data	May 29, 2020	Few-Shot Learningimage-classification	CodeCode Available	0
High-order structure preserving graph neural network for few-shot learning	May 29, 2020	Few-Shot LearningGraph Neural Network	CodeCode Available	0
Language Models are Few-Shot Learners	May 28, 2020	answerability predictionArticles	CodeCode Available	3
Boosting Few-Shot Learning With Adaptive Margin Loss	May 28, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
Looking back to lower-level information in few-shot learning	May 27, 2020	Few-Shot LearningMeta-Learning	—Unverified	0
SSM-Net for Plants Disease Identification in Low Data Regime	May 27, 2020	Few-Shot LearningTransfer Learning	CodeCode Available	0
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition	May 26, 2020	Few-Shot LearningHandwriting Recognition	—Unverified	0
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors	May 23, 2020	ClassificationFew-Shot Learning	—Unverified	0
One of these (Few) Things is Not Like the Others	May 22, 2020	BenchmarkingFew-Shot Learning	—Unverified	0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning	May 21, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption	May 19, 2020	Deep Reinforcement LearningFew-Shot Learning	—Unverified	0
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning	May 19, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification	May 18, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
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

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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