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 2551–2575 of 2964 papers

Title	Date	Tasks	Status
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method	Mar 9, 2019	Few-Shot LearningNode Classification	—Unverified
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning	May 6, 2024	Few-Shot LearningMeta-Learning	—Unverified
Invariance-Guided Feature Evolution for Few-Shot Learning	Sep 29, 2021	domain classificationFew-Shot Learning	—Unverified
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning	Nov 24, 2019	Few-Shot LearningMeta-Learning	—Unverified
Inverse is Better! Fast and Accurate Prompt for Slot Tagging	Nov 16, 2021	Few-Shot LearningPrediction	—Unverified
Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis	Oct 9, 2024	Few-Shot Learning	—Unverified
Investigating grammatical abstraction in language models using few-shot learning of novel noun gender	Mar 15, 2024	DecoderFew-Shot Learning	—Unverified
Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models	May 21, 2024	Binary ClassificationFew-Shot Learning	—Unverified
Investigating Vision-Language Model for Point Cloud-based Vehicle Classification	Apr 10, 2025	Autonomous DrivingClassification	—Unverified
IPNET:Influential Prototypical Networks for Few Shot Learning	Aug 19, 2022	Few-Shot LearningMeta-Learning	—Unverified
Iris: An AI-Driven Virtual Tutor For Computer Science Education	May 9, 2024	Few-Shot Learning	—Unverified
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks	Jan 30, 2025	Brain Computer InterfaceClassification	—Unverified
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?	Apr 2, 2024	Drug DiscoveryFew-Shot Learning	—Unverified
IsOBS: An Information System for Oracle Bone Script	Oct 1, 2020	Few-Shot LearningRetrieval	—Unverified
Is Pre-training Truly Better Than Meta-Learning?	Jun 24, 2023	DiversityFew-Shot Learning	—Unverified
Is Support Set Diversity Necessary for Meta-Learning?	Nov 28, 2020	DiversityFew-Shot Learning	—Unverified
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?	Apr 2, 2025	Action RecognitionAll	—Unverified
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?	Feb 27, 2020	Few-Shot LearningMeta-Learning	—Unverified
Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles	Nov 27, 2023	Few-Shot LearningZero-Shot Learning	—Unverified
It's About Time: Incorporating Temporality in Retrieval Augmented Language Models	Jan 24, 2024	Few-Shot LearningHallucination	—Unverified
IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT	Mar 28, 2021	Few-Shot LearningPrediction	—Unverified
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset	Dec 7, 2022	Cross-Lingual TransferFew-Shot Learning	—Unverified
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading	Mar 6, 2024	Few-Shot LearningIn-Context Learning	—Unverified
JASMINE: Arabic GPT Models for Few-Shot Learning	Dec 21, 2022	Few-Shot Learning	—Unverified
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering	May 6, 2022	Contrastive LearningExtractive Question-Answering	—Unverified

Show:10 25 50

← PrevPage 103 of 119Next →

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