The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict super-resolution rain movies in various regions of Europe when low-resolution satellite contexts covering wider regions are given. In this paper, we show that a general baseline 3D U-Net can be significantly improved with region-conditioned layers as well as orthogonality regularizations on 1x1x1 convolutional layers. Additionally, we facilitate the generalization with a bag of training strategies: mixup data augmentation, self-distillation, and feature-wise linear modulation (FiLM). Presented modifications outperform the baseline algorithms (3D U-Net) by up to 19.54% with less than 1% additional parameters, which won the 4th place in the core test leaderboard.

翻译：天气4Cast竞赛(由NeurIPS 2022主办)要求竞争者预测欧洲各地区的超分辨率雨景,如果提供覆盖大区域的低分辨率卫星环境的话。在本文中,我们表明,一般基线3D U-Net可以随着区域限制层以及1x1x1相联层的正方位调整而大大改进。此外,我们协助将培训战略包包包加以概括:数据增强、自我蒸馏和地貌精细线性调制(FILM)。 提出修改,使基线算法(3D U-Net)比19.54%高出最高19.54%,附加参数不到1%,在核心试验领头板中赢得了第4位。