  Multi-domain image-to-image translation is a problem where the goal is to
learn mappings among multiple domains. This problem is challenging in terms of
scalability because it requires the learning of numerous mappings, the number
of which increases proportional to the number of domains. However, generative
adversarial networks (GANs) have emerged recently as a powerful framework for
this problem. In particular, label-conditional extensions (e.g., StarGAN) have
become a promising solution owing to their ability to address this problem
using only a single unified model. Nonetheless, a limitation is that they rely
on the availability of large-scale clean-labeled data, which are often
laborious or impractical to collect in a real-world scenario. To overcome this
limitation, we propose a novel model called the label-noise robust
image-to-image translation model (RMIT) that can learn a clean label
conditional generator even when noisy labeled data are only available. In
particular, we propose a novel loss called the virtual cycle consistency loss
that is able to regularize cyclic reconstruction independently of noisy labeled
data, as well as we introduce advanced techniques to boost the performance in
practice. Our experimental results demonstrate that RMIT is useful for
obtaining label-noise robustness in various settings including synthetic and
real-world noise.
