A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging
作者:
Highlights:
• A regularisation model based on noise perturbation for convolutional neural networks.
• Accelerated convergence speed, circumvent over-fitting, and improved generalisation.
• Applying additive noise to earlier convolutional layers achieves better performance.
摘要
•A regularisation model based on noise perturbation for convolutional neural networks.•Accelerated convergence speed, circumvent over-fitting, and improved generalisation.•Applying additive noise to earlier convolutional layers achieves better performance.
论文关键词:Convolutional Neural Network,Regularisation,Generalisation,Weight perturbation
论文评审过程:Received 19 February 2019, Revised 8 January 2020, Accepted 8 January 2020, Available online 22 January 2020, Version of Record 19 February 2020.
论文官网地址:https://doi.org/10.1016/j.eswa.2020.113196