Domain generalization aims at training machine learning models to perform robustly across different and unseen domains. Several recent methods use multiple datasets to train models to extract domain-invariant features, hoping to generalize to unseen domains. Instead, first we explicitly train domain-dependant representations by using ad-hoc batch normalization layers to collect independent domain's statistics. Then, we propose to use these statistics to map domains in a shared latent space, where membership to a domain can be measured by means of a distance function. At test time, we project samples from an unknown domain into the same space and infer properties of their domain as a linear combination of the known ones. We apply the same mapping strategy at training and test time, learning both a latent representation and a powerful but lightweight ensemble model. We show a significant increase in classification accuracy over current state-of-the-art techniques on popular domain generalization benchmarks: PACS, Office-31 and Office-Caltech.
翻译:广域化的目的是培训机器学习模型,以便在不同和看不见的领域中强有力地发挥作用。 几个最近的方法使用多个数据集来培训模型, 以提取域变量特征, 希望将其推广到无形领域。 相反, 首先我们明确培训域依赖性代表, 使用特设集成标准化层收集独立域的统计资料。 然后, 我们提议使用这些统计数据来绘制共享潜在空间的域图, 从而可以通过远程功能测量加入域的范围。 在测试时, 我们从未知域将样本投射到同一个空间, 并推断出其域域的属性, 作为已知域的线性组合。 我们在培训和测试时应用相同的绘图战略, 学习一种潜在代表和强力但轻量的组合模型。 我们显示在流行域通用基准( PACS、 Office- 31 和 Office-Caltech)上, 相对于当前最先进的技术的分类精确度显著提高。