# Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition

arXiv: https://arxiv.org/abs/2212.02059 · HTML: https://mlsanity.com/paper/2212.02059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification](https://mlsanity.com/paper/2206.08671/) (2022)
- [Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation](https://mlsanity.com/paper/2202.10555/) (2022)
- [Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data](https://mlsanity.com/paper/2210.12853/) (2022)

## Built on by (most cited first, 4 of 4)

- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting](https://mlsanity.com/paper/2406.04867/) (2024)
- [Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning](https://mlsanity.com/paper/2305.05023/) (2023)
- [SFANet: Spatial-Frequency Attention Network for Weather Forecasting](https://mlsanity.com/paper/2405.18849/) (2024)

## Abstract (arXiv)

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.