Computation-Skipping Mask Generation for Super-Resolution Networks
Tuan Nghia Nguyen, Xuan Truong Nguyen, Kyujoong Lee, Hyuk‐Jae Lee · 2022
This study introduces a computation-skipping mask (CSM) generation framework to reduce redundant computations of Super-Resolution (SR) neural networks. For a layer in a given network, the CSM framework adds a tiny module to predict all zero locations in its output feature maps, so that their related operations can be skipped during inference. The experimental results show that CSM reduces the computational cost up to 58%, with negligible performance degradation for various SR models.