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 3471–3480 of 10307 papers

Title	Date	Tasks	Status
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey	Apr 7, 2023	Activity RecognitionDomain Adaptation	—Unverified
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning	Apr 7, 2023	Self-Supervised LearningTransfer Learning	—Unverified
Local Rose Breeds Detection System Using Transfer Learning Techniques	Apr 7, 2023	Transfer Learning	—Unverified
Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression	Apr 7, 2023	C++ codePrivacy Preserving	CodeCode Available
DATE: Domain Adaptive Product Seeker for E-commerce	Apr 7, 2023	Domain AdaptationRetrieval	CodeCode Available
Graph Enabled Cross-Domain Knowledge Transfer	Apr 7, 2023	Decision MakingRepresentation Learning	—Unverified
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone	Apr 6, 2023	DiagnosticTransfer Learning	—Unverified
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks	Apr 6, 2023	ClassificationTransfer Learning	—Unverified
Efficient Audio Captioning Transformer with Patchout and Text Guidance	Apr 6, 2023	Audio captioningCaption Generation	—Unverified
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning	Apr 6, 2023	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