论文标题
连接对抗攻击和适应领域适应的最佳运输
Connecting adversarial attacks and optimal transport for domain adaptation
论文作者
论文摘要
我们提出了一种使用最佳传输的域适应性域适应的新型算法。在域的适应中，目标是将在源域样本上训练的分类器调整为目标域。在我们的方法中，我们使用最佳传输将目标样本映射到名为Source Fiction的域。该域与源不同，但由源域分类器准确地分类。我们的主要思想是通过在目标域上通过C周期单调转换来生成源虚构。如果两个域中具有相同标签的样品是C-周期性单调的，则这些域之间的最佳传输图保留了阶级结构，这是域适应性的主要目标。为了生成一个源虚构域，我们提出了一种基于我们发现的对抗性攻击是数据集的C周期单调转换的算法。我们在数字和现代Office-31数据集上进行实验，并为所有适应任务提供简单离散的最佳运输求解器的性能改进。
We present a novel algorithm for domain adaptation using optimal transport. In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the target domain. In our method, we use optimal transport to map target samples to the domain named source fiction. This domain differs from the source but is accurately classified by the source domain classifier. Our main idea is to generate a source fiction by c-cyclically monotone transformation over the target domain. If samples with the same labels in two domains are c-cyclically monotone, the optimal transport map between these domains preserves the class-wise structure, which is the main goal of domain adaptation. To generate a source fiction domain, we propose an algorithm that is based on our finding that adversarial attacks are a c-cyclically monotone transformation of the dataset. We conduct experiments on Digits and Modern Office-31 datasets and achieve improvement in performance for simple discrete optimal transport solvers for all adaptation tasks.
