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 2701–2710 of 10307 papers

Title	Date	Tasks	Status	Hype
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach	Oct 8, 2023	Lifelong learningReinforcement Learning (RL)	—Unverified	0
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain	Oct 8, 2023	Time SeriesTime Series Forecasting	CodeCode Available	1
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks	Oct 8, 2023	Deep Reinforcement LearningTransfer Learning	—Unverified	0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset	Oct 8, 2023	text-to-speechText to Speech	—Unverified	0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision	Oct 8, 2023	Transfer Learning	CodeCode Available	0
Tight Rates in Supervised Outlier Transfer Learning	Oct 7, 2023	Outlier DetectionTransfer Learning	—Unverified	0
Transferable Deep Clustering Model	Oct 7, 2023	ClusteringDeep Clustering	—Unverified	0
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification	Oct 7, 2023	Domain AdaptationObject	CodeCode Available	0
EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT	Oct 7, 2023	Continual LearningDrift Detection	—Unverified	0
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection	Oct 7, 2023	Fake Image DetectionImage Generation	—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