论文标题
Mask Cyclegan：未配对的多模式域翻译，具有可解释的潜在变量
Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent Variable
论文作者
论文摘要
我们提出了Mask Cyclegan，这是一种基于Cyclegan构建的未配对图像域翻译的新型体系结构，目的是解决两个问题：1）图像翻译中的单偶性和2）缺乏潜在变量的解释性。我们在技术方法中的创新由三个关键组成部分组成：掩盖方案，发电机和客观。实验结果表明，该体系结构能够以可控的方式为生成的图像带来变化，并且对不同的掩模具有合理的稳健性。
We propose Mask CycleGAN, a novel architecture for unpaired image domain translation built based on CycleGAN, with an aim to address two issues: 1) unimodality in image translation and 2) lack of interpretability of latent variables. Our innovation in the technical approach is comprised of three key components: masking scheme, generator and objective. Experimental results demonstrate that this architecture is capable of bringing variations to generated images in a controllable manner and is reasonably robust to different masks.
