Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to the conventional DG that strictly requires the availability of multiple source domains, this paper considers a more realistic yet challenging scenario, namely Single Domain Generalization (Single-DG), where only one source domain is available for training. In this scenario, the limited diversity may jeopardize the model generalization on unseen target domains. To tackle this problem, we propose a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones. More specifically, we adopt a tractable upper bound of mutual information (MI) between the generated and source samples and perform a two-step optimization iteratively: (1) by minimizing the MI upper bound approximation for each sample pair, the generated images are forced to be diversified from the source samples; (2) subsequently, we maximize the MI between the samples from the same semantic category, which assists the network to learn discriminative features from diverse-styled images. Extensive experiments on three benchmark datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 25.14%.
翻译:域通用 (DG) 旨在将在多种源(即培训) 域上培训的模型推广为分布性不同的目标(即测试) 域。 与严格需要多个源域的常规 DG 相比,本文件认为一种更现实但富有挑战性的设想方案, 即单一域通用( Single- DG), 只有一个源域可供培训使用。 在这种设想方案下, 有限的多样性可能危及对隐性目标域的模型通用化。 为了解决这一问题, 我们提议了一个样式合成模块, 将不同分布的图像合成成对源域。 更具体地说, 我们采用了生成的样本和源样本之间可伸缩的相互信息的上限( MI), 并同时进行两步优化:(1) 通过最大限度地减少每对样本的MI 上层缩放近似, 产生的图像被迫从源样本中多样化; (2) 随后, 我们尽可能扩大同一语系类别中的样本之间的MI, 从而协助网络从不同类型图像中学习区别性特征的特性。 14 通过单个的25 模型, 超越了我们的标准 。