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
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Hua Qi,Qing Guo,Felix Juefei-Xu,Xiaofei Xie,Lei Ma,Wei Feng,Yang Liu,Jianjun Zhao +7 more
- 12 Oct 2020
TL;DR: DeepRhythm as discussed by the authors uses dual-spatial-temporal attention to adapt to dynamically changing face and fake types to detect DeepFakes by monitoring the heartbeat rhythms of real faces.
143
Deep Generative Models in Engineering Design: A Review
18 Mar 2022
TL;DR: Deep generative models (DGMs) typically leverage deep networks to learn from an input dataset and synthesize new designs as discussed by the authors , and have shown promising results in design applications like structural optimization, materials design, and shape synthesis.
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation
01 Jun 2022
TL;DR: Yudeng et al. as discussed by the authors proposed a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume.
MIPGAN—Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN
Haoyu Zhang,Sushma Venkatesh,Raghavendra Ramachandra,Kiran B. Raja,Naser Damer,Christoph Busch +5 more
- 14 Apr 2021
TL;DR: The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution.
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
Hao Chen,Yaohui Wang,Benoit Lagadec,Antitza Dantcheva,Francois Bremond +4 more
- 01 Jun 2021
TL;DR: Li et al. as discussed by the authors proposed a mesh-based view generator to generate novel views of a person and proposed a view-invariant loss to facilitate contrastive learning between original and generated views.
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ImageNet Large Scale Visual Recognition Challenge
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