As deep learning-based systems have become an integral part of everyday life, limitations in their generalization ability have begun to emerge. Machine learning algorithms typically rely on the i.i.d. assumption, meaning that their training and validation data are expected to follow the same distribution, which does not necessarily hold in practice. In the case of image classification, one frequent reason that algorithms fail to generalize is that they rely on spurious correlations present in training data, such as associating image styles with target classes. These associations may not be present in the unseen test data, leading to significant degradation of their effectiveness. In this work, we attempt to mitigate this Domain Generalization (DG) problem by training a robust feature extractor which disregards features attributed to image-style but infers based on style-invariant image representations. To achieve this, we train CycleGAN models to learn the different styles present in the training data and randomly mix them together to create samples with novel style attributes to improve generalization. Experimental results on the PACS DG benchmark validate the proposed method.

翻译：随着基于深度学习的系统成为日常生活的重要组成部分，其泛化能力的局限性开始显现。机器学习算法通常依赖于独立同分布假设，即期望其训练和验证数据遵循相同的分布，但这在实践中未必成立。在图像分类任务中，算法未能泛化的一个常见原因是它们依赖于训练数据中存在的虚假相关性，例如将图像风格与目标类别关联起来。这些关联可能在未见过的测试数据中并不存在，从而导致其性能显著下降。在本工作中，我们尝试通过训练一个鲁棒的特征提取器来缓解这一领域泛化问题，该提取器忽略图像风格相关的特征，而基于风格不变的图像表示进行推理。为实现这一目标，我们训练CycleGAN模型以学习训练数据中存在的不同风格，并随机混合这些风格以创建具有新颖风格属性的样本，从而提升泛化能力。在PACS领域泛化基准测试上的实验结果验证了所提方法的有效性。