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 1691–1700 of 10307 papers

Title	Date	Tasks	Status
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning	May 5, 2025	Computational EfficiencyDeep Reinforcement Learning	—Unverified
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks	Nov 17, 2018	Transfer Learning	—Unverified
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks	Jul 27, 2022	Dimensionality ReductionObject Recognition	—Unverified
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT	Feb 21, 2023	Prompt EngineeringTransfer Learning	—Unverified
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition	Feb 8, 2023	graph constructionTransfer Learning	—Unverified
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer	Dec 15, 2024	Feature EngineeringLanguage Modeling	—Unverified
A probabilistic constrained clustering for transfer learning and image category discovery	Jun 28, 2018	ClusteringConstrained Clustering	—Unverified
Active Multitask Learning with Committees	Mar 24, 2021	Transfer Learning	—Unverified
Conceptual Expansion Neural Architecture Search (CENAS)	Oct 7, 2021	Neural Architecture SearchTransfer Learning	—Unverified
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models	Jan 13, 2024	Deep LearningStructural Health Monitoring	—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