论文标题
Weather4cast竞赛的区域条件正交3D U-NET
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
论文作者
论文摘要
Weather4cast竞赛（由Neurips 2022主持）要求竞争对手在涵盖更广泛地区的低分辨率卫星环境时，预测欧洲各个地区的超分辨率雨电影。在本文中，我们表明，通过区域条件层以及1x1x1卷积层的正交性正规化，可以显着改善一般基线3D U-NET。此外，我们通过一袋培训策略来促进概括：混合数据增强，自distillation和特征线性调制（电影）。提出的修改优于基线算法（3D U-NET），最多超过19.54％，额外的参数少于1％，在核心测试排行榜中赢得了第四名。
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.
