mlsanityData to 7 Oct 2026

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

arXiv 2212.02059 ↗First posted 5 Dec 2022cs.CVcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 5 Dec 2022.

Builds on

9 of 16 references.

References · 9

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  2. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  6. 2017Deep Learning for Precipitation Nowcasting: A Benchmark and A New Modelcited by 180
  7. 2022FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classificationcited by 26
  8. 2022Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimationcited by 10
  9. 2022Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Datacited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2025Deep Learning and Foundation Models for Weather Prediction: A Surveycited by 15
  2. 2024Deep learning for precipitation nowcasting: A survey from the perspective of time series forecastingcited by 14
  3. 2023Domain Agnostic Image-to-image Translation using Low-Resolution Conditioningcited by 0
  4. 2024SFANet: Spatial-Frequency Attention Network for Weather Forecastingcited by 0