Convergence of non-convex non-concave GANs using sinkhorn divergence

• Main Authors: Adnan, Risman, Saputra, Muchlisin Adi, Fadlil, Junaidillah, Ezerman, Martianus Frederic, Iqbal, Muhamad, Basaruddin, Tjan
• 其他作者: School of Physical and Mathematical Sciences
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Science::Physics; Convergence; Generative Adversarial Network
• 在線閱讀: https://hdl.handle.net/10356/154075

Sinkhorn divergence is a symmetric normalization of entropic regularized optimal transport. It is a smooth and continuous metrized weak-convergence with excellent geometric properties. We use it as an alternative for the minimax objective function in formulating generative adversarial networks. The...