论文标题
无监督的潜在空间翻译网络
Unsupervised Latent Space Translation Network
论文作者
论文摘要
在计算机视觉中经常讨论的一个任务是将图像从一个域的映射到一个称为图像到图像翻译的另一个域中的相应图像。目前有几种解决此任务的方法。在本文中，我们提出了单位框架的增强，有助于消除其主要缺点。更具体地说，我们在所使用的潜在表示上引入了一个附加的对抗歧视器，而不是VAE，该域将两个域的潜在空间分布都相似。在MNIST和USPS域的适应任务上，这种方法极大地超过了竞争的方法。
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Currently there are several approaches solving this task. In this paper, we present an enhancement of the UNIT framework that aids in removing its main drawbacks. More specifically, we introduce an additional adversarial discriminator on the latent representation used instead of VAE, which enforces the latent space distributions of both domains to be similar. On MNIST and USPS domain adaptation tasks, this approach greatly outperforms competing approaches.
