Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2821–2830 of 10307 papers

Title	Date	Tasks	Status
Deep Transfer Learning: Model Framework and Error Analysis	Oct 12, 2024	image-classificationImage Classification	—Unverified
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations	Feb 17, 2022	Transfer Learning	—Unverified
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition	Mar 14, 2022	Activity RecognitionFew-Shot Learning	—Unverified
Deep Transfer Learning with Joint Adaptation Networks	May 21, 2016	Multi-Source Unsupervised Domain AdaptationTransfer Learning	—Unverified
Deep Transfer Learning with Ridge Regression	Jun 11, 2020	regressionTransfer Learning	—Unverified
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application	Apr 18, 2018	Domain AdaptationFault Diagnosis	—Unverified
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning	Jan 8, 2025	Decision MakingInductive Learning	—Unverified
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules	Sep 30, 2018	reinforcement-learningReinforcement Learning	—Unverified
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study	Mar 11, 2021	Medical Code PredictionTransfer Learning	—Unverified
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders	Sep 14, 2017	Ensemble LearningTransfer Learning	—Unverified

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All datasets Office-Home 100 sleep nights of 8 caregivers BanglaLekha Isolated Dataset COCO70 KITTI Object Tracking Evaluation 2012 Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	APCLIP	Accuracy	84.2	—	Unverified
2	DFA-ENT	Accuracy	69.2	—	Unverified
3	DFA-SAFN	Accuracy	69.1	—	Unverified
4	EasyTL	Accuracy	63.3	—	Unverified
5	MEDA	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10-20% Mask PSNR	3.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Chatterjee, Dutta et al.[1]	Accuracy	96.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Co-Tuning	Accuracy	85.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Physical Access	EER	5.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	riadd.aucmedi	AUROC	0.95	—	Unverified