mlsanityData to 14 Oct 2026

Systematic Analysis and Removal of Circular Artifacts for StyleGAN

arXiv 2103.01090 ↗First posted 1 Mar 2021cs.CVcited by 1

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Abstract (arXiv)

StyleGAN is one of the state-of-the-art image generators which is well-known for synthesizing high-resolution and hyper-realistic face images. Though images generated by vanilla StyleGAN model are visually appealing, they sometimes contain prominent circular artifacts which severely degrade the quality of generated images. In this work, we provide a systematic investigation on how those circular artifacts are formed by studying the functionalities of different stages of vanilla StyleGAN architecture, with both mechanism analysis and extensive experiments. The key modules of vanilla StyleGAN that promote such undesired artifacts are highlighted. Our investigation also explains why the artifacts are usually circular, relatively small and rarely split into 2 or more parts. Besides, we propose a simple yet effective solution to remove the prominent circular artifacts for vanilla StyleGAN, by applying a novel pixel-instance normalization (PIN) layer.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

14 of 19 references.

References · 14

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  4. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  6. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  7. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  8. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  9. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 944
  10. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  11. 2016Coupled Generative Adversarial Networkscited by 216
  12. 2018Batch-Instance Normalization for Adaptively Style-Invariant Neural Networkscited by 78
2 more
  1. 2017Towards the Automatic Anime Characters Creation with Generative Adversarial Networkscited by 29
  2. 2019Switchable Normalization for Learning-to-Normalize Deep Representationcited by 16

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022DiffGAR: Model-Agnostic Restoration from Generative Artifacts Using Image-to-Image Diffusion Modelscited by 5