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 2961–2970 of 10307 papers

Title	Date	Tasks	Status	Hype
On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Framework	Aug 7, 2023	Transfer Learning	—Unverified	0
A Transfer Learning Framework for Proactive Ramp Metering Performance Assessment	Aug 7, 2023	Transfer Learning	—Unverified	0
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies	Aug 7, 2023	Region ProposalTransfer Learning	—Unverified	0
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding	Aug 6, 2023	AllTransfer Learning	—Unverified	0
Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control	Aug 5, 2023	Deep Reinforcement Learningreinforcement-learning	—Unverified	0
One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer	Aug 5, 2023	Contrastive LearningKnowledge Distillation	CodeCode Available	1
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision	Aug 5, 2023	DiversityRepresentation Learning	—Unverified	0
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities	Aug 4, 2023	Graph structure learningKnowledge Distillation	CodeCode Available	1
Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain	Aug 4, 2023	Transfer Learning	—Unverified	0
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs	Aug 4, 2023	Knowledge DistillationQuantization	CodeCode Available	1

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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