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 2991–3000 of 10307 papers

Title	Date	Tasks	Status	Score
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation	Jul 6, 2019	BenchmarkingDomain Adaptation	CodeCode Available	5
Exploring Driving-aware Salient Object Detection via Knowledge Transfer	May 18, 2021	Objectobject-detection	CodeCode Available	5
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution	Nov 1, 2021	coreference-resolutionCoreference Resolution	CodeCode Available	5
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Jan 26, 2025	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions	Feb 8, 2023	Depth EstimationDepth Prediction	CodeCode Available	5
On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic Topology	Nov 8, 2023	Representation LearningTransfer Learning	CodeCode Available	5
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning	May 19, 2022	Continual LearningTransfer Learning	CodeCode Available	5
Explainable Action Advising for Multi-Agent Reinforcement Learning	Nov 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks	Feb 8, 2022	Computational Efficiencyfeature selection	CodeCode Available	5
Exclusive Supermask Subnetwork Training for Continual Learning	Oct 18, 2022	Continual LearningText Classification	CodeCode Available	5

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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