mlsanityData to 7 Oct 2026

GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks

arXiv 2207.08320 ↗First posted 17 Jul 2022cs.HC cs.AI cs.LGcited by 7

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Abstract (arXiv)

Generative Adversarial Network (GAN) is widely adopted in numerous application areas, such as data preprocessing, image editing, and creativity support. However, GAN's 'black box' nature prevents non-expert users from controlling what data a model generates, spawning a plethora of prior work that focused on algorithm-driven approaches to extract editing directions to control GAN. Complementarily, we propose a GANzilla: a user-driven tool that empowers a user with the classic scatter/gather technique to iteratively discover directions to meet their editing goals. In a study with 12 participants, GANzilla users were able to discover directions that (i) edited images to match provided examples (closed-ended tasks) and that (ii) met a high-level goal, e.g., making the face happier, while showing diversity across individuals (open-ended tasks).

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 17 Jul 2022.

Builds on

18 of 31 references.

References · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  4. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  5. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 691
  6. 2020GANSpace: Discovering Interpretable GAN Controlscited by 612
  7. 2020Closed-Form Factorization of Latent Semantics in GANscited by 407
  8. 2016Generating Videos with Scene Dynamicscited by 376
  9. 2020StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generationcited by 331
  10. 2020Unsupervised Discovery of Interpretable Directions in the GAN Latent Spacecited by 288
  11. 2019On the "steerability" of generative adversarial networkscited by 247
  12. 2019GANalyze: Toward Visual Definitions of Cognitive Image Propertiescited by 193
6 more
  1. 2020Editing in Style: Uncovering the Local Semantics of GANscited by 183
  2. 2021EditGAN: High-Precision Semantic Image Editingcited by 156
  3. 2019Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesiscited by 106
  4. 2020Controlling generative models with continuous factors of variationscited by 93
  5. 2018Sequential Attention GAN for Interactive Image Editingcited by 33
  6. 2022GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Informationcited by 13

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Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 1.

7 papers.

  1. 2024Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooMcited by 77
  2. 2023Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Modelscited by 67
  3. 2024PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinementcited by 61
  4. 2023Next Steps for Human-Centered Generative AI: A Technical Perspectivecited by 13
  5. 2023Bias and Diversity in Synthetic-based Face Recognitioncited by 11
  6. 2023GANravel: User-Driven Direction Disentanglement in Generative Adversarial Networkscited by 5
  7. 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Designcited by 5