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GANs for Image Generation

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

Deep learning has proven to be hugely successful in computer vision and has even been applied to many real-world tasks, such as image classification (He et al. 2016), object detection (Ren et al. 2015), and segmentation (Long et al. 2015). Compared with these tasks in supervised learning, however, image generation, which belongs to unsupervised learning, may not achieve the desired performance. The target of image generation is to learn to draw pictures by means of some generative models, as shown in Fig. 2.1.

Suggested Citation

  • Xudong Mao & Qing Li, 2021. "GANs for Image Generation," Springer Books, in: Generative Adversarial Networks for Image Generation, chapter 0, pages 9-52, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-6048-8_2
    DOI: 10.1007/978-981-33-6048-8_2
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