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 4531–4540 of 10307 papers

Title	Date	Tasks	Status	Hype
When does Bias Transfer in Transfer Learning?	Jul 6, 2022	Transfer Learning	CodeCode Available	1
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms	Jul 5, 2022	Federated LearningTransfer Learning	—Unverified	0
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer	Jul 5, 2022	Image-text matchingKnowledge Distillation	CodeCode Available	1
A Unified Meta-Learning Framework for Dynamic Transfer Learning	Jul 5, 2022	Meta-LearningTransfer Learning	CodeCode Available	0
Vision-and-Language Pretraining	Jul 5, 2022	Diversityimage-classification	CodeCode Available	0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning	Jul 5, 2022	Auxiliary LearningDenoising	—Unverified	0
Factorizing Knowledge in Neural Networks	Jul 4, 2022	DisentanglementTransfer Learning	CodeCode Available	1
A Robust Ensemble Model for Patasitic Egg Detection and Classification	Jul 4, 2022	ClassificationData Augmentation	—Unverified	0
Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss	Jul 4, 2022	Transfer Learning	—Unverified	0
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction	Jul 4, 2022	Graph Neural NetworkPrediction	—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