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 2811–2820 of 10307 papers

Title	Date	Tasks	Status
Deep Learning for Bias Detection: From Inception to Deployment	Oct 12, 2021	Bias DetectionDeep Learning	—Unverified
Deep transfer learning for system identification using long short-term memory neural networks	Apr 6, 2022	Transfer Learning	—Unverified
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology	Apr 3, 2020	ClassificationDiagnostic	—Unverified
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings	Nov 8, 2019	Domain AdaptationTransfer Learning	—Unverified
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition	Apr 15, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified
Deep Transfer Learning For Whole-Brain fMRI Analyses	Jul 2, 2019	Transfer Learning	—Unverified
Deep Transfer Learning for WiFi Localization	Mar 8, 2021	Transfer Learning	—Unverified
Bayesian Optimization of Bilevel Problems	Dec 24, 2024	Bayesian OptimizationBilevel Optimization	—Unverified
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP	Apr 1, 2019	Continual LearningTransfer Learning	—Unverified
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders	Sep 14, 2017	Ensemble LearningTransfer 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