We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. This makes it possible to reliably detect if an image is generated by a particular network. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. Stylegan2 - StyleGAN2 - Official TensorFlow ImplementationĪbstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling.
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