Deep Recursive Residual Network

Year: 2,017
Authors: Ying Tai, Jian Yang, Xiaoming Liu
Journal:  IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Programming languages: C, Matlab, Shell

Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image SuperResolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks. Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks; recursive learning is used to control the model parameters while increasing the depth.

Sign In


Reset Password

Please enter your username or email address, you will receive a link to create a new password via email.