Analyzing and Improving the Image Quality of StyleGAN
Tero Karras,Samuli Laine,Miika Aittala,Janne Hellsten,Jaakko Lehtinen,Timo Aila +5 more
- 14 Jun 2020
- pp 8110-8119
TL;DR: In this paper, the authors propose to redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images.
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Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. 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. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.
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Citations
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
01 Jun 2022
TL;DR: SemanticStyleGAN as discussed by the authors uses a generator to model local semantic parts separately and synthesize images in a compositional way, where the structure and texture of different local parts are controlled by corresponding latent codes.
NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fields
Kaiwen Jiang,Shu-Yu Chen,Feng-Lin Liu,Hongbo Fu,Lin Gao +4 more
- 15 Nov 2022
TL;DR: NeRFFaceEditing as mentioned in this paper enables editing and decoupling geometry and appearance in the pretrained triplane-based neural radiance field while retaining its high quality and fast inference speed.
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DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
Marcel C. Bühler,Andrés Romero,Radu Timofte +2 more
- 30 Nov 2020
TL;DR: This work proposes a novel framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution, and is the first method to leverage semantic maps for explorative super- resolution, and validates DeepSEE for up to 32x magnification and exploration of the space ofsuper-resolution.
InsetGAN for Full-Body Image Generation
01 Jun 2022
TL;DR: In this article , the authors propose a method to combine multiple pretrained GANs, where one GAN generates a global canvas (e.g., human body) and a set of specialized GAN or insets, focus on different parts that can be seamlessly inserted onto the global canvas.
Generative Adversarial Networks in Finance: An Overview
TL;DR: In this paper, the authors present an overview of how GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry.
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