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 226–250 of 2964 papers

Title	Date	Tasks	Status	Hype
Overthinking the Truth: Understanding how Language Models Process False Demonstrations	Jul 18, 2023	Few-Shot Learning	CodeCode Available	1
Towards Task Sampler Learning for Meta-Learning	Jul 18, 2023	DiversityFew-Shot Learning	CodeCode Available	1
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation	Jul 14, 2023	Aspect-Based Sentiment AnalysisAttribute	CodeCode Available	1
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning	Jul 6, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection	Jul 2, 2023	DisentanglementFew-Shot Learning	CodeCode Available	1
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation	Jun 30, 2023	Few-Shot LearningPrompt Learning	CodeCode Available	1
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion	Jun 26, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations	Jun 25, 2023	Few-Shot LearningQuestion Answering	CodeCode Available	1
Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning	Jun 18, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Federated Few-shot Learning	Jun 17, 2023	ArticlesFederated Learning	CodeCode Available	1
FewSAR: A Few-shot SAR Image Classification Benchmark	Jun 16, 2023	ClassificationFew-Shot Learning	CodeCode Available	1
Neural Fine-Tuning Search for Few-Shot Learning	Jun 15, 2023	Few-Shot LearningNeural Architecture Search	CodeCode Available	1
Few-shot bioacoustic event detection at the DCASE 2023 challenge	Jun 15, 2023	Event DetectionFew-Shot Learning	CodeCode Available	1
EventCLIP: Adapting CLIP for Event-based Object Recognition	Jun 10, 2023	Few-Shot LearningObject	CodeCode Available	1
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems	Jun 3, 2023	Few-Shot LearningKeyword Spotting	CodeCode Available	1
Consistency-guided Prompt Learning for Vision-Language Models	Jun 1, 2023	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
Task-Equivariant Graph Few-shot Learning	May 30, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
Deeply Coupled Cross-Modal Prompt Learning	May 29, 2023	Domain AdaptationFew-Shot Learning	CodeCode Available	1
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification	May 29, 2023	Few-Shot Learningimage-classification	CodeCode Available	1
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning	May 28, 2023	Few-Shot LearningKnowledge Distillation	CodeCode Available	1
Training on Thin Air: Improve Image Classification with Generated Data	May 24, 2023	Data AugmentationFew-Shot Learning	CodeCode Available	1
Sentiment Analysis in the Era of Large Language Models: A Reality Check	May 24, 2023	Aspect-Based Sentiment AnalysisFew-Shot Learning	CodeCode Available	1
Improving few-shot learning-based protein engineering with evolutionary sampling	May 23, 2023	Few-Shot LearningProtein Design	CodeCode Available	1
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?	May 22, 2023	Data-free Knowledge DistillationFew-Shot Learning	CodeCode Available	1
Small Language Models Improve Giants by Rewriting Their Outputs	May 22, 2023	Few-Shot LearningIn-Context 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