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 3951–3960 of 10307 papers

Title	Date	Tasks	Status
Comparison of fine-tuning strategies for transfer learning in medical image classification	Jun 14, 2024	image-classificationImage Classification	—Unverified
Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler	Jul 1, 2022	ClassificationHate Speech Detection	—Unverified
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning	Apr 14, 2024	Federated Learningmodel	—Unverified
Evaluation of Federated Learning in Phishing Email Detection	Jul 27, 2020	Distributed ComputingFederated Learning	—Unverified
Federated Adversarial Domain Adaptation	Nov 5, 2019	DisentanglementDomain Adaptation	—Unverified
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms	Jul 5, 2022	Federated LearningTransfer Learning	—Unverified
Federated and Transfer Learning for Cancer Detection Based on Image Analysis	May 30, 2024	Federated LearningTransfer Learning	—Unverified
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes	Jul 24, 2024	Federated LearningGaussian Processes	—Unverified
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks	Sep 22, 2024	Continual LearningTransfer Learning	—Unverified
Frustratingly Easy Transferability Estimation	Jun 17, 2021	Mutual Information EstimationTransfer Learning	—Unverified

Show:10 25 50

← PrevPage 396 of 1031Next →

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