mlsanityData to 14 Oct 2026

Semantic Unfolding of StyleGAN Latent Space

arXiv 2206.14892 ↗First posted 29 Jun 2022cs.CV cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image. This editing property emerges from the disentangled nature of the latent space. In this paper, we identify that the facial attribute disentanglement is not optimal, thus facial editing relying on linear attribute separation is flawed. We thus propose to improve semantic disentanglement with supervision. Our method consists in learning a proxy latent representation using normalizing flows, and we show that this leads to a more efficient space for face image editing.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 22 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

18 of 22 references.

References · 18

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  6. 2016Density estimation using Real NVPcited by 2,042
  7. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  8. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 695
  9. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  10. 2020GANSpace: Discovering Interpretable GAN Controlscited by 614
  11. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  12. 2020InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANscited by 394
6 more
  1. 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
  2. 2020Editing in Style: Uncovering the Local Semantics of GANscited by 183
  3. 2016Inverting The Generator Of A Generative Adversarial Networkcited by 140
  4. 2020Generative Hierarchical Features from Synthesizing Imagescited by 83
  5. 2021A Latent Transformer for Disentangled Face Editing in Images and Videoscited by 67
  6. 2020Collaborative Learning for Faster StyleGAN Embeddingcited by 65

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 2.

4 papers.

  1. 2023Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groupscited by 6
  2. 2023Causal Image Synthesis of Brain MR in 3Dcited by 0
  3. 2026RIVET: Robust Idempotent Voice Attribute Editingcited by 0
  4. 2026Encoder-Decoder Manifold Alignment for Idempotent Generationcited by 0