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 2931–2940 of 10307 papers

Title	Date	Tasks	Status
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique	Jul 10, 2021	image-classificationImage Classification	—Unverified
Detection under Privileged Information	Mar 31, 2016	Face RecognitionMalware Classification	—Unverified
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning	Dec 2, 2014	Multiple Instance LearningRepresentation Learning	—Unverified
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks	Nov 3, 2023	Transfer Learning	—Unverified
Determination of the critical points for systems of directed percolation class using machine learning	Jul 19, 2023	Transfer Learning	—Unverified
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery	Jun 3, 2017	Data AugmentationObject Recognition	—Unverified
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter	Mar 23, 2023	AllNeural Architecture Search	—Unverified
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach	Nov 17, 2018	reinforcement-learningReinforcement Learning	—Unverified
Developing Conversational Data and Detection of Conversational Humor in Telugu	Nov 1, 2021	Transfer LearningWord Embeddings	—Unverified
A Data-Driven Approach to Improve 3D Head-Pose Estimation	Jan 1, 2022	Face RecognitionHead Pose Estimation	—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