  Unpaired image-to-image translation using Generative Adversarial Networks
(GAN) is successful in converting images among multiple domains. Moreover,
recent studies have shown a way to diversify the outputs of the generator.
However, since there are no restrictions on how the generator diversifies the
results, it is likely to translate some unexpected features. In this paper, we
propose Style-Restricted GAN (SRGAN) to demonstrate the importance of
controlling the encoded features used in style diversifying process. More
specifically, instead of KL divergence loss, we adopt three new losses to
restrict the distribution of the encoded features: batch KL divergence loss,
correlation loss, and histogram imitation loss. Further, the encoder is
pre-trained with classification tasks before being used in translation process.
The study reports quantitative as well as qualitative results with Precision,
Recall, Density, and Coverage. The proposed three losses lead to the
enhancement of the level of diversity compared to the conventional KL loss. In
particular, SRGAN is found to be successful in translating with higher
diversity and without changing the class-unrelated features in the CelebA face
dataset. To conclude, the importance of the encoded features being
well-regulated was proven with two experiments. Our implementation is available
at https://github.com/shinshoji01/Style-Restricted_GAN.
