2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MMSDF-2.8
Paper Title Systematic Analysis of Circular Artifacts for StyleGAN
Authors Way Tan, National University of Singapore, Singapore; Bihan Wen, Nanyang Technological University, Singapore; Cen Chen, Zeng Zeng, Xulei Yang, Institute for Infocomm Research (I2R), the Agency for Science, Technology and Research (A*STAR), Singapore
SessionSS-MMSDF-2: Special Session: AI for Multimedia Security and Deepfake 2
LocationArea A
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Special Sessions: Artificial Intelligence for Multimedia Security and Deepfake
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent research works have pointed out that the synthesized images by StyleGAN 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 modules that are used in the StyleGAN architecture. We present both analysis of the StyleGAN mechanism and extensive experiments to verify our claims. The key modules of StyleGAN that promote such undesired artifacts are highlighted based on the analysis. Besides, we propose a simple yet effective solution to remove the prominent circular artifacts for StyleGAN, by applying a simple but efficient pixel-instance normalization layer. The improved StyleGAN model trained via our proposed approach successfully prevents the appearance of circular artifacts in the generated images.