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
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Disentangled Inference for GANs with Latently Invertible Autoencoder
TL;DR: This paper shows that the entanglement of the latent space for the VAE/GAN framework poses the main challenge for encoder learning, and proposes a novel algorithm named Latently Invertible Autoencoder (LIA), which is based on an invertible network and its inverse mapping that is symmetrically embedded in the latentspace of VAE.
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CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image
Pengyi Zhang,Yunxin Zhong,Yulin Deng,Xiaoying Tang,Xiaoqiong Li +4 more
- 03 Nov 2020
TL;DR: A novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions is proposed, which has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of CO VID-19 pandemic.
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VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang,Yinan He,Jiashuo Yu,Fan Zhang,Chenyang Si,Yuming Jiang,Yuanhan Zhang,Tianxing Wu,Qingyang Jin,Nattapol Chanpaisit,Yaohui Wang,Xinyuan Chen,Limin Wang,Dahua Lin,Yu Qiao,Ziwei Liu +15 more
- 16 Jun 2024
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High Resolution Face Age Editing
Xu Yao,Gilles Puy,Alasdair Newson,Yann Gousseau,Pierre Hellier +4 more
- 10 Jan 2021
TL;DR: HRFAE as discussed by the authors uses an encoder-decoder architecture to encode a face image to age-invariant features and learn a modulation vector corresponding to a target age.
36
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
TL;DR: Continual Representation using Distillation (CoReD) as mentioned in this paper employs the concept of Continual Learning (CoL), Representation Learning (ReL), and Knowledge Distillation to perform sequential domain adaptation tasks on new deepfake and GAN-generated synthetic face datasets.
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