# Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift

arXiv: https://arxiv.org/abs/2212.02968 · HTML: https://mlsanity.com/paper/2212.02968/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [MetNet: A Neural Weather Model for Precipitation Forecasting](https://mlsanity.com/paper/2003.12140/) (2020)
- [MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather](https://mlsanity.com/paper/2111.09954/) (2021)

## Built on by (most cited first, 7 of 7)

- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Implicit Stacked Autoregressive Model for Video Prediction](https://mlsanity.com/paper/2303.07849/) (2023)
- [Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting](https://mlsanity.com/paper/2406.04867/) (2024)
- [ACE Metric: Advection and Convection Evaluation for Accurate Weather Forecasting](https://mlsanity.com/paper/2406.04678/) (2024)
- [Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network](https://mlsanity.com/paper/2303.04405/) (2023)
- [Learning Robust Precipitation Forecaster by Temporal Frame Interpolation](https://mlsanity.com/paper/2311.18341/) (2023)
- [Masked Autoregressive Model for Weather Forecasting](https://mlsanity.com/paper/2409.20117/) (2024)

## Abstract (arXiv)

Deep learning-based weather prediction models have advanced significantly in recent years. However, data-driven models based on deep learning are difficult to apply to real-world applications because they are vulnerable to spatial-temporal shifts. A weather prediction task is especially susceptible to spatial-temporal shifts when the model is overfitted to locality and seasonality. In this paper, we propose a training strategy to make the weather prediction model robust to spatial-temporal shifts. We first analyze the effect of hyperparameters and augmentations of the existing training strategy on the spatial-temporal shift robustness of the model. Next, we propose an optimal combination of hyperparameters and augmentation based on the analysis results and a test-time augmentation. We performed all experiments on the W4C22 Transfer dataset and achieved the 1st performance.