Unsupervised Semantic Segmentation Inpainting Network Using a Generative Adversarial Network with Preprocessing

Woo Jin Ahn, Dong Won Kim, Tae Koo Kang, Dong Sung Pae, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

Abstract

The generative adversarial neural network has shown a novel result in the image generation area. However, applying it to a semantic segmentation inpainting task exhibits instability due to the different data distribution. To solve this problem, we propose an unsupervised semantic segmentation inpainting method using an adversarial deep neural network with a newly introduced preprocessing method and loss function. For stabilizing the adversarial training for semantic segmentation inpainting, we match the probability distribution of the segmentation maps with the developed preprocessing method. In addition, a new cross-entropy total variation loss for the probability map is introduced to improve the segmentation inpainting work by smoothing the segmentation map. The experimental results demonstrate the proposed algorithm’s effectiveness on both synthetic and real datasets.

Original languageEnglish
Article number781
JournalApplied Sciences (Switzerland)
Volume13
Issue number2
DOIs
Publication statusPublished - 2023 Jan

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • binary total variation
  • convolutional neural network
  • data preprocessing
  • deeplearning

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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