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 3621–3630 of 10307 papers

Title	Date	Tasks	Status
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring	Oct 2, 2017	EEGElectroencephalogram (EEG)	—Unverified
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning	Mar 23, 2021	Transfer LearningVulnerability Detection	—Unverified
A Primer on Pretrained Multilingual Language Models	Jul 1, 2021	Joint Multilingual Sentence RepresentationsMultilingual text classification	—Unverified
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition	Apr 8, 2020	Gesture RecognitionTransfer Learning	—Unverified
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning	Feb 10, 2016	General Classificationimage-classification	—Unverified
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems	May 26, 2023	Transfer Learning	—Unverified
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation	Jan 1, 2023	Contrastive LearningDomain Adaptation	—Unverified
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective	Jun 9, 2015	ClassificationGeneral Classification	—Unverified
Deep CNNs for large scale species classification	Feb 3, 2021	ClassificationGeneral Classification	—Unverified
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition	Sep 12, 2021	Anatomical Landmark DetectionFacial Landmark Detection	—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