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 2671–2680 of 10307 papers

Title	Date	Tasks	Status	Hype
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches	Oct 12, 2023	Defect DetectionModel Optimization	—Unverified	0
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformity	Oct 12, 2023	Data AugmentationEthics	CodeCode Available	0
Self-supervised visual learning for analyzing firearms trafficking activities on the Web	Oct 12, 2023	Classificationimage-classification	—Unverified	0
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation	Oct 12, 2023	Decision MakingDomain Adaptation	—Unverified	0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning	Oct 12, 2023	Continual LearningMeta-Learning	—Unverified	0
A Recent Survey of Heterogeneous Transfer Learning	Oct 12, 2023	SurveyTransfer Learning	CodeCode Available	1
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain Adaptation	Oct 11, 2023	Domain AdaptationRepresentation Learning	CodeCode Available	0
DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks	Oct 11, 2023	Deep Reinforcement Learningreinforcement-learning	—Unverified	0
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains	Oct 11, 2023	Transfer Learning	—Unverified	0
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets	Oct 11, 2023	EthicsRepresentation Learning	—Unverified	0

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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