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 1451–1460 of 10307 papers

Title	Date	Tasks	Status	Hype
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans	Jul 16, 2024	SpecificityTransfer Learning	—Unverified	0
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process	Jul 15, 2024	Cantilever BeamTransfer Learning	—Unverified	0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning	Jul 15, 2024	Q-Learningreinforcement-learning	—Unverified	0
Accessing Vision Foundation Models at ImageNet-level Costs	Jul 15, 2024	Knowledge DistillationTransfer Learning	CodeCode Available	2
Detecting Omissions in Geographic Maps through Computer Vision	Jul 15, 2024	Transfer Learning	CodeCode Available	1
Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Function	Jul 15, 2024	Pose EstimationTransfer Learning	—Unverified	0
Order parameters and phase transitions of continual learning in deep neural networks	Jul 14, 2024	Continual LearningTransfer Learning	—Unverified	0
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection	Jul 14, 2024	3D Object DetectionAutonomous Driving	CodeCode Available	1
Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the "torch for R" ecosystem	Jul 13, 2024	BenchmarkingDeep Learning	—Unverified	0
Combining Federated Learning and Control: A Survey	Jul 12, 2024	Decision MakingFederated 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