论文标题
Otadapt：无监督域适应的最佳基于运输的方法
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation
论文作者
论文摘要
无监督的域适应性是计算机视觉中具有挑战性的问题之一。本文提出了一种基于最佳运输距离的无监督域适应的新方法。我们的方法允许在无需跨域的有意义指标的情况下对目标和源域对齐。此外，该建议可以将源域和目标域之间的正确映射关联，并保证源域和目标域之间拓扑的限制。在不同问题的不同数据集上评估了所提出的方法，即（i）MNIST，MNIST-M，USPS数据集的数字识别，（ii）Amazon，网络摄像头，DSLR和VISDA数据集的对象识别，（III）IP102 DataSet上的昆虫识别。实验结果表明，我们提出的方法始终提高性能准确性。此外，我们的框架可以与其他CNN框架合并到端到端的深层网络设计中，以提高其性能。
Unsupervised domain adaptation is one of the challenging problems in computer vision. This paper presents a novel approach to unsupervised domain adaptations based on the optimal transport-based distance. Our approach allows aligning target and source domains without the requirement of meaningful metrics across domains. In addition, the proposal can associate the correct mapping between source and target domains and guarantee a constraint of topology between source and target domains. The proposed method is evaluated on different datasets in various problems, i.e. (i) digit recognition on MNIST, MNIST-M, USPS datasets, (ii) Object recognition on Amazon, Webcam, DSLR, and VisDA datasets, (iii) Insect Recognition on the IP102 dataset. The experimental results show that our proposed method consistently improves performance accuracy. Also, our framework could be incorporated with any other CNN frameworks within an end-to-end deep network design for recognition problems to improve their performance.
