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More Key Applications of GANs

In: Generative Adversarial Networks for Image Generation

Author

Listed:
  • Xudong Mao

    (Hong Kong Polytechnic University, Department of Computing)

  • Qing Li

    (Hong Kong Polytechnic University, Department of Computing)

Abstract

In the previous chapter, we learned that GANs are very powerful for image generation. In this chapter, we learn three interesting applications of GANs: image-to-image translation, unsupervised domain adaptation, and GANs for security. One type of GANs application is for tasks that require high-quality images, such as image-to-image translation. To improve the output image quality, a discriminator is introduced to judge whether the output images are realistic. Another type is to extend the use of the discriminator in a more generalized way, such as unsupervised domain adaptation. For instance, the task of unsupervised domain adaptation includes two domain datasets, the source domain and the target domain. To learn indistinguishable feature representations for the source and target domains, we can introduce a discriminator to judge whether the output features come from the source domain or the target domain.

Suggested Citation

  • Xudong Mao & Qing Li, 2021. "More Key Applications of GANs," Springer Books, in: Generative Adversarial Networks for Image Generation, chapter 0, pages 53-74, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-6048-8_3
    DOI: 10.1007/978-981-33-6048-8_3
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