Image Attribute Migration Based on Decoupling and Adaptive Layer Instance Normalization

The issue of image attribute migration is one of the hot research topics in the field of computer vision, which has received extensive research interest. However, current unsupervised image attribute migration models using symmetric generative adversarial network structure do not work well on datasets with large geometric variations, where the results lack diversity and are of low quality. To address these problems, we present an image attribute migration model based on decoupling and adaptive layer instance normalization. First, a codec structure based on a decoupled representation is constructed as the generator, and an adaptive layer instance normalization operation is used in the decoder. Then, the iterations of the model are constrained by various improved loss functions. We conducted controlled experiments and compared the results of our method with other methods using several datasets with large geometric variations. The experimental results demonstrate that the proposed method can achieve high quality and diverse image attribute migration.