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 4601–4610 of 10307 papers

Title	Date	Tasks	Status	Hype
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications	Jun 21, 2022	Depth EstimationStereo Depth Estimation	—Unverified	0
Supervised learning of random quantum circuits via scalable neural networks	Jun 21, 2022	Transfer Learning	CodeCode Available	0
Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo	Jun 21, 2022	Dependency ParsingPart-Of-Speech Tagging	CodeCode Available	0
KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences	Jun 21, 2022	Human Part SegmentationTransfer Learning	CodeCode Available	0
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network	Jun 20, 2022	Deep Learningimage-classification	—Unverified	0
Deep representation of EEG data from Spatio-Spectral Feature Images	Jun 20, 2022	EEGElectroencephalogram (EEG)	—Unverified	0
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification	Jun 20, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
A Neural Network Based Method with Transfer Learning for Genetic Data Analysis	Jun 20, 2022	Transfer Learning	—Unverified	0
Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets	Jun 20, 2022	Data AugmentationTransfer Learning	CodeCode Available	0
An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models	Jun 20, 2022	Transfer Learning	—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