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 1701–1725 of 2964 papers

Title	Date	Tasks	Status	Hype
Automated Few-Shot Time Series Forecasting based on Bi-level Programming	Mar 7, 2022	Decision MakingFew-Shot Learning	—Unverified	0
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification	Mar 4, 2022	counterfactualFew-Shot Learning	CodeCode Available	0
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning	Mar 3, 2022	Domain AdaptationFew-Shot Learning	—Unverified	0
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning	Mar 3, 2022	Few-Shot LearningMeta-Learning	—Unverified	0
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models	Mar 3, 2022	Few-Shot LearningRepresentation Learning	—Unverified	0
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels	Mar 3, 2022	Anomaly DetectionCardiac Segmentation	CodeCode Available	1
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation	Feb 27, 2022	Data AugmentationDisentanglement	CodeCode Available	0
Interpretable Concept-based Prototypical Networks for Few-Shot Learning	Feb 27, 2022	Few-Shot LearningMeta-Learning	—Unverified	0
CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing	Feb 21, 2022	Few-Shot LearningSentence	CodeCode Available	2
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?	Feb 18, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning	Feb 18, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
Semantically Proportional Patchmix for Few-Shot Learning	Feb 17, 2022	Few-Shot LearningTransfer Learning	—Unverified	0
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization	Feb 15, 2022	Event DetectionEvent Extraction	—Unverified	0
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation	Feb 14, 2022	Few-Shot LearningSegmentation	—Unverified	0
Cross Domain Few-Shot Learning via Meta Adversarial Training	Feb 11, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
A Modern Self-Referential Weight Matrix That Learns to Modify Itself	Feb 11, 2022	Few-Shot Learning	CodeCode Available	1
Bias-Eliminated Semantic Refinement for Any-Shot Learning	Feb 10, 2022	Few-Shot LearningGeneralized Zero-Shot Learning	CodeCode Available	1
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding	Feb 9, 2022	Few-Shot LearningMNLI-m	CodeCode Available	1
Cedille: A large autoregressive French language model	Feb 7, 2022	Few-Shot LearningLanguage Modeling	CodeCode Available	2
MAML and ANIL Provably Learn Representations	Feb 7, 2022	DiversityFew-Shot Learning	—Unverified	0
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach	Feb 5, 2022	Few-Shot Learning	CodeCode Available	0
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning	Feb 5, 2022	class-incremental learningClass Incremental Learning	—Unverified	0
Smoothed Embeddings for Certified Few-Shot Learning	Feb 2, 2022	Adversarial RobustnessFew-Shot Learning	CodeCode Available	0
Advances in MetaDL: AAAI 2021 challenge and workshop	Feb 1, 2022	Few-Shot Learning	—Unverified	0
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty	Feb 1, 2022	Cross-Domain Few-Shotcross-domain few-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