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 1476–1500 of 2964 papers

Title	Date	Tasks	Status
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
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models	Oct 12, 2020	Few-Shot LearningSentence	—Unverified
STT: Soft Template Tuning for Few-Shot Learning	Jan 16, 2022	Few-Shot LearningLanguage Modeling	—Unverified
STT: Soft Template Tuning for Few-Shot Adaptation	Jul 18, 2022	Few-Shot LearningLanguage Modeling	—Unverified
Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks	Apr 25, 2022	Few-Shot LearningIn-Context Learning	—Unverified
Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation	May 13, 2024	Few-Shot LearningImage Segmentation	—Unverified
Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning	Feb 27, 2024	Few-Shot Learning	—Unverified
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification	May 15, 2023	AllFew-Shot Image Classification	—Unverified
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification	Sep 29, 2021	Cross-Domain Few-ShotDomain Generalization	—Unverified
Synthesized Annotation Guidelines are Knowledge-Lite Boosters for Clinical Information Extraction	Apr 1, 2025	Few-Shot Learningnamed-entity-recognition	—Unverified
Synthetic Examples Improve Generalization for Rare Classes	Apr 11, 2019	Few-Shot LearningSelf-Driving Cars	—Unverified
Ta-Adapter: Enhancing few-shot CLIP with task-aware encoders	Apr 29, 2024	Few-Shot Learningimage-classification	—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