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 101–125 of 2964 papers

Title	Date	Tasks	Status	Hype
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels	Oct 11, 2019	Bayesian InferenceDomain Adaptation	CodeCode Available	1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach	Feb 11, 2021	Data AugmentationDeep Learning	CodeCode Available	1
Covariate Distribution Aware Meta-learning	Jul 6, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Cross-Domain Few-Shot Learning by Representation Fusion	Oct 13, 2020	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Convolutional Bypasses Are Better Vision Transformer Adapters	Jul 14, 2022	Few-Shot LearningInductive Bias	CodeCode Available	1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation	Sep 18, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
Cost-effective Instruction Learning for Pathology Vision and Language Analysis	Jul 25, 2024	Few-Shot LearningLanguage Modelling	CodeCode Available	1
Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment	Feb 23, 2025	Cross-Domain Few-ShotCross-Domain Few-Shot Object Detection	CodeCode Available	1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study	Mar 6, 2020	Few-Shot LearningModel Selection	CodeCode Available	1
Constellation Nets for Few-Shot Learning	Jan 1, 2021	ClusteringFew-Shot Image Classification	CodeCode Available	1
A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition	Sep 26, 2020	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	1
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models	Sep 21, 2024	Few-Shot LearningInstruction Following	CodeCode Available	1
Contrastive Meta-Learning for Partially Observable Few-Shot Learning	Jan 30, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
Contrastive Prototypical Network with Wasserstein Confidence Penalty	Oct 21, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining	Feb 15, 2024	AllFew-Shot Learning	CodeCode Available	1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification	Aug 19, 2021	Causal Inferencecounterfactual	CodeCode Available	1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters	Aug 18, 2021	Few-Shot LearningTransfer Learning	CodeCode Available	1
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid	Oct 30, 2022	Few-Shot LearningSelection bias	CodeCode Available	1
A Closer Look at Few-Shot 3D Point Cloud Classification	Mar 31, 2023	3D Point Cloud ClassificationFew-Shot 3D Point Cloud Classification	CodeCode Available	1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network	Aug 20, 2024	Action ClassificationAction Classification (1-shot)	CodeCode Available	1
Context-enriched molecule representations improve few-shot drug discovery	Apr 24, 2023	Drug DiscoveryFew-Shot Learning	CodeCode Available	1
Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection	Jul 16, 2024	Fake News DetectionFew-Shot Learning	CodeCode Available	1
A Closer Look at Few-shot Classification	Apr 8, 2019	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
Data Distributional Properties Drive Emergent In-Context Learning in Transformers	Apr 22, 2022	Few-Shot LearningIn-Context Learning	CodeCode Available	1
CoNeRF: Controllable Neural Radiance Fields	Dec 3, 2021	3D Face Modelling3D Reconstruction	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