Modular Domain-to-Domain Translation Network

We present a method for constructing and training a deep domain-to-domain translation network: two datasets describing the same classes (i.e. the source and target domains) are used to train a deep network that can translate a pattern coming from the source domain to its counterpart form in the target domain. We introduce the development of a hierarchical architecture that encapsulates information of the target domain by embedding individually trained networks. This deep hierarchical architecture is then trained as one unified deep network. Using this approach, we prove that samples from the original domain are translated to the target domain format for both the cases where there is a one-to-one correspondence in the samples of the two domains and also when this correspondence information is absent. In our experiments we get a good translation operation as long as the target domain dataset provides good classification results when trained alone. We use either some distorted version of the MNIST dataset or the SVHN dataset as the original domain for the translation task and the MNIST as the target domain. The translation from one information domain to the other is visualized and evaluated. We also discuss the proposed model’s relation to the conditional Generative Adversarial Networks and we further argue that deep learning can benefit from such forms of strict hierarchical architectures.