论文标题
将分类语义集成到无监督的域翻译中
Integrating Categorical Semantics into Unsupervised Domain Translation
论文作者
论文摘要
尽管最近无监督的域翻译（UDT）最近取得了很大的成功，但我们认为，通过分类语义特征介导其翻译可以扩大其适用性。特别是，我们证明了分类语义改善了共享多个对象类别的感知不同域之间的翻译。我们提出了一种以无监督的方式学习的方法，即源域和目标域不变的分类语义特征（例如对象标签）。我们表明，在学习的分类语义上调节了无监督域翻译方法的样式编码器，导致了一项翻译，可保留MNIST $ \ leftrightArrow $ svhn上的数字，并在Sketch $ \ to $ to $ yros上进行更真实的样式。
While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical semantic features could broaden its applicability. In particular, we demonstrate that categorical semantics improves the translation between perceptually different domains sharing multiple object categories. We propose a method to learn, in an unsupervised manner, categorical semantic features (such as object labels) that are invariant of the source and target domains. We show that conditioning the style encoder of unsupervised domain translation methods on the learned categorical semantics leads to a translation preserving the digits on MNIST$\leftrightarrow$SVHN and to a more realistic stylization on Sketches$\to$Reals.
