Image-to-image translation is a class of tasks which translate an image to another image of the specified type. In essence, it is a mapping problem from pixels to pixels.However, existing methods have showed limitation in term of scalability and robustness when it comes to translation tasks for more than two domains.In order to achieve translation results of high quality and high efficiency, an unpaired image-to-image translation based on conditional projection is proposed in this paper. The proposed method calculates the similarity between the feature information learned by generator and conditional information, which can improve the accuracy of translation and qualities of generated images. Compared to existing models, the proposed method adopts fewer parameters and shorter training time. The effectiveness of the proposed model is shown on multiple datasets.

generative adversarial networks; image-to-image translation; supervised learning; conditional projection; artificial intelligence