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 2076–2100 of 2964 papers

Title	Date	Tasks	Status
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks	Mar 30, 2024	Few-Shot LearningInstruction Following	—Unverified
Self-Supervised Open-Ended Classification with Small Visual Language Models	Sep 30, 2023	Few-Shot LearningImage Captioning	—Unverified
Long Context Compression with Activation Beacon	Jan 7, 2024	4kdocument understanding	—Unverified
Sociocultural knowledge is needed for selection of shots in hate speech detection tasks	Apr 4, 2023	Few-Shot LearningHate Speech Detection	—Unverified
Sparse annotation strategies for segmentation of short axis cardiac MRI	Jul 24, 2023	Data AugmentationFew-Shot Learning	—Unverified
Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification	Mar 19, 2025	DiagnosticFew-Shot Learning	—Unverified
Resisting Large Data Variations via Introspective Transformation Network	May 16, 2018	Data AugmentationFew-Shot Learning	—Unverified
SPeCiaL: Self-Supervised Pretraining for Continual Learning	Jun 16, 2021	continual few-shot learningContinual Learning	—Unverified
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization	Mar 24, 2023	Abstractive Text SummarizationFew-Shot Learning	—Unverified
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data	Mar 25, 2021	Autonomous DrivingFew-Shot Learning	—Unverified
SPKLIP: Aligning Spike Video Streams with Natural Language	May 19, 2025	Contrastive LearningFew-Shot Learning	—Unverified
Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video	Jun 21, 2024	BenchmarkingFew-Shot Learning	—Unverified
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification	Jun 1, 2019	Data AugmentationFew-Shot Image Classification	—Unverified
SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition	Apr 3, 2024	ClassificationFew-Shot Learning	—Unverified
Stance Detection on Social Media with Fine-Tuned Large Language Models	Apr 18, 2024	Few-Shot LearningStance Detection	—Unverified
State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects	Jan 2, 2025	BenchmarkingFace Swapping	—Unverified
BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection	Jul 27, 2022	Few-Shot LearningVocal Bursts Type Prediction	—Unverified
Statistically and Computationally Efficient Linear Meta-representation Learning	Dec 1, 2021	Few-Shot LearningRepresentation Learning	—Unverified
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction	Oct 31, 2023	Domain AdaptationFew-Shot Learning	—Unverified
Stochastic Prototype Embeddings	Sep 25, 2019	Few-Shot Learningopen-set classification	—Unverified
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool	Apr 1, 2021	Few-Shot LearningLanguage Modeling	—Unverified
STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification	Oct 29, 2022	Few-Shot LearningFew-Shot Text Classification	—Unverified
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling	Jul 1, 2022	Contrastive LearningData Augmentation	—Unverified
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning	Jun 4, 2024	Few-Shot LearningIntrusion Detection	—Unverified
Stress Testing of Meta-learning Approaches for Few-shot Learning	Jan 21, 2021	Few-Shot LearningMeta-Learning	—Unverified

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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