Proceedings of the Korean Society of Broadcast Engineers Conference (한국방송∙미디어공학회:학술대회논문집)
- 2020.11a
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- Pages.234-236
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- 2020
Content-Adaptive Model Update of Convolutional Neural Networks for Super-Resolution
- Ki, Sehwan (Korea Advanced Institute of Science and Technology Dep. Of Electronic Engineering) ;
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Kim, Munchurl
(Korea Advanced Institute of Science and Technology Dep. Of Electronic Engineering)
- Published : 2020.11.28
Abstract
Content-adaptive training and transmission of the model parameters of neural networks can boost up the SR performance with higher restoration fidelity. In this case, efficient transmission of neural network parameters are essentially needed. Thus, we propose a novel method of compressing the network model parameters based on the training of network model parameters in the sense that the residues of filter parameters and content loss are jointly minimized. So, the residues of filter parameters are only transmitted to receiver sides for different temporal portions of video under consideration. This is advantage for image restoration applications with receivers (user terminals) of low complexity. In this case, the user terminals are assumed to have a limited computation and storage resource.
Keywords