Training Super-Resolution Network with Difficulty-Based Adaptive Sampling
Jiajun Li, Zhong‐Qiu Zhao · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
The performance of super-resolution (SR) networks is highly dependent on the quality and size of the training data. However, research on better use of the available SR dataset remains unexplored. In this work, we propose Difficulty-based Adaptive Sampling (DAS) strategy to fill this gap. Specifically, to further exploit the input samples, DAS first uses a Calculating and Sorting module (CS module) to calculate the upsampling difficulty of the input samples and sorts them. The CS module makes DAS be efficient with an online form and using a fast method to calculate the difficulty degrees of samples. Then it uses a Sampler module to select appropriate samples. Finally, with a Recorder module to record the training states, DAS can dynamically select the samples suitable for different training stages. Extensive experiments demonstrate that DAS selecting the appropriate samples for each iteration can effectively improve the performance of SR networks.