Image-to-image Local Feature Translation Using Double Adversarial Networks Based on CycleGAN

摘要

Image-to-image translation is a hot field in the machine learning with the emergency of the generative adversarial networks. Most of the latest models easily lead to changes in the overall image and over-fitting when they are used to local feature translation. To address these limitations, this article adds a suppressor and proposes a double adversarial CycleGAN. The suppressor is added to suppress the change of images, and the suppressor and generator form a new adversarial relationship. We hope it will achieve Nash equilibrium that is the change of image focus on the local feature. Finally, a contrast experiment was conducted. In the case of image local feature transfer, the change of image is focused on the local features and the overfitting phenomenon can be well resolved.