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 1126–1150 of 2964 papers

Title	Date	Tasks	Status	Score
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification	Aug 22, 2019	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	CodeCode Available	5
Few-Shot Multilingual Open-Domain QA from 5 Examples	Feb 27, 2025	Few-Shot LearningOpen-Domain Question Answering	CodeCode Available	5
Cooperative Bi-path Metric for Few-shot Learning	Aug 10, 2020	ClassificationFew-Shot Learning	CodeCode Available	5
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning	Apr 2, 2023	Few-Shot LearningInductive Learning	CodeCode Available	5
Improving Meta-Learning Generalization with Activation-Based Early-Stopping	Aug 3, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	5
Improved transferability of self-supervised learning models through batch normalization finetuning	Aug 15, 2024	ClassificationFew-Shot Learning	CodeCode Available	5
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing	Jul 20, 2020	Drug DiscoveryFew-Shot Learning	CodeCode Available	5
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation	Oct 10, 2022	counterfactualData Augmentation	CodeCode Available	5
A Large Encoder-Decoder Family of Foundation Models For Chemical Language	Jul 24, 2024	DecoderFew-Shot Learning	CodeCode Available	5
Attentional Meta-learners for Few-shot Polythetic Classification	Jun 9, 2021	Classificationfeature selection	CodeCode Available	5
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks	May 23, 2023	Few-Shot LearningLanguage Modeling	CodeCode Available	5
Improved Visually Prompted Keyword Localisation in Real Low-Resource Settings	Sep 9, 2024	Few-Shot Learning	CodeCode Available	5
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples	Feb 23, 2024	Dataset GenerationDecoder	CodeCode Available	5
Instance-level Few-shot Learning with Class Hierarchy Mining	Apr 15, 2023	Few-shot Instance SegmentationFew-Shot Learning	CodeCode Available	5
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments	Nov 26, 2018	Autonomous NavigationDomain Adaptation	CodeCode Available	5
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models	Jul 22, 2023	ArticlesClassification	CodeCode Available	5
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation	Feb 2, 2024	Few-Shot LearningNeRF	CodeCode Available	5
A Language Agent for Autonomous Driving	Nov 17, 2023	Autonomous DrivingCommon Sense Reasoning	CodeCode Available	5
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression	Oct 16, 2021	Few-Shot LearningKnowledge Distillation	CodeCode Available	5
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning	Jun 8, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	5
Few-Shot Learning with Graph Neural Networks	Jan 1, 2018	Active LearningFew-Shot Learning	CodeCode Available	5
HQP: A Human-Annotated Dataset for Detecting Online Propaganda	Apr 28, 2023	Few-Shot LearningPropaganda detection	CodeCode Available	5
Few-Shot Learning with Graph Neural Networks	Nov 10, 2017	Active LearningCross-Domain Few-Shot	CodeCode Available	5
Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventricles	May 9, 2024	DiagnosticFew-Shot Learning	CodeCode Available	5

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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