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 4691–4700 of 10307 papers

Title	Date	Tasks	Status
Learning from LDA using Deep Neural Networks	Aug 5, 2015	Document ClassificationGeneral Classification	—Unverified
A Study of the Generalizability of Self-Supervised Representations	Sep 19, 2021	Self-Supervised LearningTransfer Learning	—Unverified
Learning from Synthetic Data for Visual Grounding	Mar 20, 2024	Language ModellingLarge Language Model	—Unverified
A Study of the Tasks and Models in Machine Reading Comprehension	Jan 23, 2020	Machine Reading ComprehensionReading Comprehension	—Unverified
Multi-View Representation is What You Need for Point-Cloud Pre-Training	Jun 5, 2023	3D geometry3D Object Detection	—Unverified
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing	Sep 16, 2021	FairnessManagement	—Unverified
Learning from scarce information: using synthetic data to classify Roman fine ware pottery	Jul 3, 2021	Transfer Learning	—Unverified
A Study of Transfer Learning in Music Source Separation	Oct 23, 2020	Audio Source SeparationData Augmentation	—Unverified
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning	Nov 11, 2020	Image GenerationObject	—Unverified
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation	Jun 13, 2020	Graph GenerationObject	—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