基于条件投影的无配对数据跨域图像翻译方法

Unpaired cross-domain image-to-image translation method using conditional projection

现有的图像翻译方法在涉及两个域以上的翻译任务时缺少可拓展性和鲁棒性。为了实现高质量、高效率的翻译,提出了一种基于条件投影的无配对数据图像转换方法。该方法通过计算生成器学习到的特征信息与条件信息的相似度,来提升翻译的正确性并生成更高质量的图像。相较于现有方法,所提方法使用的参数更少、训练时间更短,并基于多个数据集验证了所提方法的有效性。

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.