English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 theresults, it is likely to translate some unexpected features. In this paper, wepropose Style-Restricted GAN (SRGAN) to demonstrate the importance ofcontrolling the encoded features used in style diversifying process. Morespecifically, instead of KL divergence loss, we adopt three new losses torestrict the distribution of the encoded features: batch KL divergence loss,correlation loss, and histogram imitation loss. Further, the encoder ispre-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 theenhancement of the level of diversity compared to the conventional KL loss. Inparticular, SRGAN is found to be successful in translating with higherdiversity and without changing the class-unrelated features in the CelebA facedataset. To conclude, the importance of the encoded features beingwell-regulated was proven with two experiments. Our implementation is availableat https://github.com/shinshoji01/Style-Restricted_GAN.

Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks