TunaGAN: Interpretable GAN for Smart Editing

Abstract

In this paper, we introduce a tunable generative adversary network (TunaGAN) that uses an auxiliary network on top of existing generator networks (Style-GAN) to modify high-resolution face images according to user's high-level instructions, with good qualitative and quantitative performance. To optimize for feature disentanglement, we also investigate two different latent space that could be traversed for modification. The problem of mode collapse is characterized in detail for model robustness. This work could be easily extended to content-aware image editor based on other GANs and provide insight on mode collapse problems in more general settings.

References in corpus (7)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Optimizing the Latent Space of Generative Networks
• GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
• Signal Recovery from Pooling Representations
• Modular Generative Adversarial Networks