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 2521–2530 of 10307 papers

Title	Date	Tasks	Status
Deep Transfer Learning with Joint Adaptation Networks	May 21, 2016	Multi-Source Unsupervised Domain AdaptationTransfer Learning	—Unverified
DeeSIL: Deep-Shallow Incremental Learning	Aug 20, 2018	Incremental LearningTransfer Learning	—Unverified
An embedding for EEG signals learned using a triplet loss	Mar 23, 2023	Brain Computer InterfaceEEG	—Unverified
Benchmarks and models for entity-oriented polarity detection	Jun 1, 2018	Sentiment AnalysisTransfer Learning	—Unverified
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings	Nov 8, 2019	Domain AdaptationTransfer Learning	—Unverified
An Embedding-Dynamic Approach to Self-supervised Learning	Jul 7, 2022	Classificationimage-classification	—Unverified
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification	Mar 3, 2020	Automated Feature EngineeringBIG-bench Machine Learning	—Unverified
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community	Sep 1, 2017	speech-recognitionSpeech Recognition	—Unverified
Deep Transfer Learning For Whole-Brain fMRI Analyses	Jul 2, 2019	Transfer Learning	—Unverified
A deep convolutional neural network for classification of Aedes albopictus mosquitoes	Oct 29, 2021	ClassificationExplainable Models	—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