mlsanityData to 7 Oct 2026

Hyperbolic Generative Adversarial Network

arXiv 2102.05567 ↗First posted 10 Feb 2021cs.LG cs.CV stat.MLcited by 5

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Abstract (arXiv)

Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture. In this study, different configurations using fully connected hyperbolic layers in the GAN, CGAN, and WGAN are tested, in what we call the HGAN, HCGAN, and HWGAN, respectively. The results are measured using the Inception Score (IS) and the Fr'echet Inception Distance (FID) on the MNIST dataset. Depending on the configuration and space curvature, better results are achieved for each proposed hyperbolic versions than their euclidean counterpart.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 10 Feb 2021.

Builds on

21 of 41 references.

References · 21

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9 more
  1. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  2. 2019Hyperbolic Graph Convolutional Neural Networkscited by 400
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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 1.

5 papers.

  1. 2023Hyperbolic Deep Learning in Computer Vision: A Surveycited by 72
  2. 2023Poincar'e ResNetcited by 24
  3. 2022The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generationcited by 22
  4. 2022Autoencoding Hyperbolic Representation for Adversarial Generationcited by 7
  5. 2024Why are hyperbolic neural networks effective? A study on hierarchical representation capabilitycited by 1