Image Generation with Self Pixel-wise Normalization

Abstract

Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique. However, since these approaches need a mask image to infer the pixel-wise affine transformation parameters, they cannot be applied to the general image generation models having no paired mask images. To resolve this problem, this paper presents a novel normalization method, called self pixel-wise normalization (SPN), which effectively boosts the generative performance by performing the pixel-adaptive affine transformation without the mask image. In our method, the transforming parameters are derived from a self-latent mask that divides the feature map into the foreground and background regions. The visualization of the selflatent masks shows that SPN effectively captures a sin-

Generic image application using GANs (Generative Adversarial Networks): A Review

“…For this purpose, the conditional opponent networks are ideal. This family of problems is solved with the Pix2pix model (Yeo et al 2022).CycleGAN (Zhu et al 2017) has expanded this method with an insufficient cycle continuity that seeks to maintain the original image following a transformation and reversal cycle. Matching pictures are no longer necessary in this formulation for training.…”