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 3711–3720 of 10307 papers

Title	Date	Tasks	Status
Adaptive Transfer Learning of Multi-View Time Series Classification	Oct 14, 2019	ClassificationDensity Estimation	—Unverified
Experimental and causal view on information integration in autonomous agents	Jun 14, 2016	Decision MakingSelf-Driving Cars	—Unverified
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms	Jul 5, 2022	Federated LearningTransfer Learning	—Unverified
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Mar 2, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices	Jan 1, 2020	Arrhythmia DetectionDenoising	—Unverified
Deep Bag-of-Sub-Emotions for Depression Detection in Social Media	Mar 1, 2021	Deep LearningDepression Detection	—Unverified
Explainable AI based Glaucoma Detection using Transfer Learning and LIME	Oct 7, 2022	Transfer Learning	—Unverified
Explainable AI for Fair Sepsis Mortality Predictive Model	Apr 19, 2024	Decision MakingFairness	—Unverified
Automatically Score Tissue Images Like a Pathologist by Transfer Learning	Sep 9, 2022	Learning TheoryTransfer Learning	—Unverified
A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce	Jun 28, 2018	Recommendation SystemsTransfer Learning	—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