GANs Conditioning Methods: A Survey

TL;DR

This survey reviews various conditioning methods for GANs, analyzing their mechanisms, theoretical foundations, and performance to guide future research in controlled image generation.

Contribution

It provides a comprehensive comparison and analysis of existing conditioning techniques for GANs, highlighting their strengths and limitations.

Findings

Different conditioning methods vary in integration complexity.

Performance of conditioning methods depends on dataset characteristics.

Insights into strengths and limitations of each method.

Abstract

In recent years, Generative Adversarial Networks (GANs) have seen significant advancements, leading to their widespread adoption across various fields. The original GAN architecture enables the generation of images without any specific control over the content, making it an unconditional generation process. However, many practical applications require precise control over the generated output, which has led to the development of conditional GANs (cGANs) that incorporate explicit conditioning to guide the generation process. cGANs extend the original framework by incorporating additional information (conditions), enabling the generation of samples that adhere to that specific criteria. Various conditioning methods have been proposed, each differing in how they integrate the conditioning information into both the generator and the discriminator networks. In this work, we review the conditioning methods proposed for GANs, exploring the characteristics of each method and highlighting their unique mechanisms and theoretical foundations. Furthermore, we conduct a comparative analysis of these methods, evaluating their performance on various image datasets. Through these analyses, we aim to provide insights into the strengths and limitations of various conditioning techniques, guiding future research and application in generative modeling.