mlsanityData to 7 Oct 2026

Manifold-preserved GANs

arXiv 2109.08955 ↗First posted 18 Sep 2021cs.CV cs.AI cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Generative Adversarial Networks (GANs) have been widely adopted in various fields. However, existing GANs generally are not able to preserve the manifold of data space, mainly due to the simple representation of discriminator for the real/generated data. To address such open challenges, this paper proposes Manifold-preserved GANs (MaF-GANs), which generalize Wasserstein GANs into high-dimensional form. Specifically, to improve the representation of data, the discriminator in MaF-GANs is designed to map data into a high-dimensional manifold. Furthermore, to stabilize the training of MaF-GANs, an operation with precise and universal solution for any K-Lipschitz continuity, called Topological Consistency is proposed. The effectiveness of the proposed method is justified by both theoretical analysis and empirical results. When adopting DCGAN as the backbone on CelebA (256*256), the proposed method achieved 12.43 FID, which outperforms the state-of-the-art model like Realness GAN (23.51 FID) by a large margin. Code will be made publicly available.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 18 Sep 2021.

Builds on

22 of 28 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Combating Mode Collapse in GANs via Manifold Entropy Estimationcited by 4
  2. 2022Robust Representation via Dynamic Feature Aggregationcited by 2