Unsupervised Multiple Domain Translation is the task of transforming data
from one domain to other domains without having paired data to train the
systems. Typically, methods based on Generative Adversarial Networks (GANs) are
used to address this task. However, our proposal exclusively relies on a
modified version of a Variational Autoencoder. This modification consists of
the use of two latent variables disentangled in a controlled way by design. One
of this latent variables is imposed to depend exclusively on the domain, while
the other one must depend on the rest of the variability factors of the data.
Additionally, the conditions imposed over the domain latent variable allow for
better control and understanding of the latent space. We empirically
demonstrate that our approach works on different vision datasets improving the
performance of other well known methods. Finally, we prove that, indeed, one of
the latent variables stores all the information related to the domain and the
other one hardly contains any domain information.

无监督多领域转换是将数据从一个领域转换到其他领域的任务，我们提出的方法基于改进的变分自编码器，利用两个分离的潜在变量以一种可控的方式实现。我们通过实验证明了我们的方法在不同的视觉数据集上可以提高其他知名方法的性能，并证明了一个潜在变量存储与领域相关的所有信息，而另一个几乎不包含任何领域信息。