DWT+DWT: Deep Learning Domain Generalization Techniques Using Discrete Wavelet Transform with Deep Whitening Transform

Abstract

Recently, there is a growing demand for a deep learning framework with robust generalization performance in real-world domains, such as an autonomous driving environment. The existing domain generalization methodologies for convolutional neural networks have been designed to actively utilize the feature map with the generative model or normalization techniques to distinguish domain-specific information. However, augmented images are essential for measuring style sensitivity. This study shows that style information can be extracted from an original image through color space separation and frequency decomposition without a separate augmented image. Therefore, it can be used as a method independent of existing network models. The proposed method shows an mIoU improvement by 1.54% compared to the existing method in the semantic segmentation model trained using urban scene datasets.