GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks

Abstract

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).

References in corpus (5)

• Learning Transferable Visual Models From Natural Language Supervision
• Generating Videos with Scene Dynamics
• Controlling generative models with continuous factors of variations
• Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis
• GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information