• Multicyclic Loss for Multidomain Image-to-Image Translation

      Reale, Michael, Chair; Confer, Amos; Andriamanalimanana, Bruno; Schneider, Ethan H.
      GANs developed to Translate an Image’s style between different domains often only care about the initial translation, and not the ability to further translate upon an image This can cause issues where, if one would want to generate upon an image and then further on, change that image even more that person may come into issues. This creates a ”gap” between the base images and the generated images, and in this paper a Multicyclic Loss is presented, where the Neural Network also trains on further translations to images that were already translated. iv