A Study on Model Compression Methods for SRGAN

Donghwi Kim, Jun-won Lee, Sang-hyo Park

Abstract: The construction of SR algorithms by using deep learning model such as super-resolution generative adversarial networks (SRGAN) have become larger and complicated model architectures with requiring a vast amount of memory capacity. However, it is difficult to operate deep learning models which have millions of parameters at the mobile devices. Thus, in this paper, we present a study on lightweight neural network using network pruning method. Through our extensive experiments, pruned network can show similar performance to the original SRGAN model with substantially reduced model size.