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 2751–2760 of 10307 papers

Title	Date	Tasks	Status
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation	Feb 1, 2024	Machine TranslationTransfer Learning	—Unverified
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification	Oct 1, 2019	Active LearningDeep Reinforcement Learning	—Unverified
Anaphoric Zero Pronoun Identification: A Multilingual Approach	Dec 1, 2020	Transfer Learning	—Unverified
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks	Oct 8, 2023	Deep Reinforcement LearningTransfer Learning	—Unverified
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes	Apr 10, 2025	Bayesian OptimizationComputational Efficiency	—Unverified
Deep reinforcement learning for guidewire navigation in coronary artery phantom	Oct 5, 2021	Deep Reinforcement LearningQ-Learning	—Unverified
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications	Dec 31, 2018	Decision MakingDeep Reinforcement Learning	—Unverified
Deep reinforcement learning for optical systems: A case study of mode-locked lasers	Jun 10, 2020	Deep Reinforcement LearningNavigate	—Unverified
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion	May 16, 2023	Deep Reinforcement Learningreinforcement-learning	—Unverified
Disentangling Transfer and Interference in Multi-Domain Learning	Jul 2, 2021	Transfer 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