Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning

TL;DR

This paper introduces a supervised learning approach to interpret and manipulate the latent space of GANs, offering more precise and robust image editing capabilities compared to traditional unsupervised methods.

Contribution

The work presents a supervised method for understanding GAN latent spaces, enabling more accurate and property-rich image manipulations, especially for task-specific applications.

Findings

Supervised approach improves image manipulation accuracy.

Method is robust and easier to implement.

Allows richer property control in generated images.

Abstract

With great progress in the development of Generative Adversarial Networks (GANs), in recent years, the quest for insights in understanding and manipulating the latent space of GAN has gained more and more attention due to its wide range of applications. While most of the researches on this task have focused on unsupervised learning method, which induces difficulties in training and limitation in results, our work approaches another direction, encoding human's prior knowledge to discover more about the hidden space of GAN. With this supervised manner, we produce promising results, demonstrated by accurate manipulation of generated images. Even though our model is more suitable for task-specific problems, we hope that its ease in implementation, preciseness, robustness, and the allowance of richer set of properties (compared to other approaches) for image manipulation can enhance the result of many current applications.