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 2001–2010 of 10307 papers

Title	Date	Tasks	Status
Channel Scaling: A Scale-and-Select Approach for Transfer Learning	Mar 22, 2021	Medical Image AnalysisTransfer Learning	—Unverified
Computational strategies for cross-species knowledge transfer and translational biomedicine	Aug 16, 2024	Transfer Learning	—Unverified
Computation and Data Efficient Backdoor Attacks	Jan 1, 2023	3D Point Cloud ClassificationData Poisoning	—Unverified
Change your singer: a transfer learning generative adversarial framework for song to song conversion	Nov 7, 2019	Transfer LearningVoice Conversion	—Unverified
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings	Sep 29, 2021	Transfer Learning	—Unverified
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone	Apr 6, 2023	DiagnosticTransfer Learning	—Unverified
Action Recognition for American Sign Language	May 20, 2022	Action RecognitionTransfer Learning	—Unverified
Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules	Dec 12, 2024	Transfer Learning	—Unverified
Assessing the Performance of Analog Training for Transfer Learning	May 16, 2025	Transfer Learning	—Unverified
Adversarial Inductive Transfer Learning with input and output space adaptation	Sep 25, 2019	Domain AdaptationMulti-Task 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