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

• URL: http://arxiv.org/abs/2212.02968v1
• Date: Tue, 6 Dec 2022 13:39:15 GMT
• Title: Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift
• Authors: Minseok Seo, Doyi Kim, Seungheon Shin, Eunbin Kim, Sewoong Ahn, Yeji Choi,
• Abstract summary: We propose a training strategy to make the weather prediction model robust to spatial-temporal shifts. We performed all experiments on the W4C22 Transfer dataset and achieved the 1st performance.
• Score: 6.994786884130848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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