The Six Fronts of the Generative Adversarial Networks
This work addresses the problem of information overload for researchers in the GAN field by offering a structured survey, though it is incremental as it builds on existing surveys by providing a more integrated approach.
The paper tackles the challenge of navigating the extensive literature on Generative Adversarial Networks by organizing it into six thematic fronts, providing a comprehensive and integrated overview to serve as an entry point for new researchers and an update for experienced ones.
Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf. In this paper, we intend to help those researchers, by splitting that incoming wave into six "fronts": Architectural Contributions, Conditional Techniques, Normalization and Constraint Contributions, Loss Functions, Image-to-image Translations, and Validation Metrics. The division in fronts organizes literature into approachable blocks, ultimately communicating to the reader how the area is evolving. Previous surveys in the area, which this works also tabulates, focus on a few of those fronts, leaving a gap that we propose to fill with a more integrated, comprehensive overview. Here, instead of an exhaustive survey, we opt for a straightforward review: our target is to be an entry point to this vast literature, and also to be able to update experienced researchers to the newest techniques.