Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks

Abstract: Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at https://github.com/PDillis/stylegan3-fun.

• The paper introduces 'Discriminator Dreaming,' a method that reuses GAN discriminators for both image manipulation and new image generation.
• It employs pre-trained StyleGAN2/3 models across diverse datasets to harness intermediate discriminator layers for artistic output.
• The study highlights challenges in computational overhead and parameter tuning, indicating opportunities for future efficiency improvements.

Discriminator Synthesis: Exploring Untapped Potential in Generative Adversarial Networks

The paper “Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks” elucidates a novel approach to leveraging the underutilized components of Generative Adversarial Networks (GANs), specifically focusing on the Discriminator. While substantial research has been dedicated to enhancing the capabilities and stability of the Generator to produce high-fidelity outputs, the Discriminator has been largely overlooked post-training. This work introduces a method termed "Discriminator Dreaming," which exploits the Discriminator's learned features from training datasets for image manipulation and generation.

Core Contributions

The paper's central contribution is the proposition of a framework that reutilizes the Discriminator in innovative ways. Historically, the Discriminator's role has been primarily adversarial, tasked with differentiating real from fake data. However, this research demonstrates that the insights garnered during this process can be repurposed artistically. It draws inspiration from projects like those of Robbie Barrat and endeavors utilizing large models such as CLIP, emphasizing untapped creative potential within model architecture.

Two primary applications are discussed: firstly, altering existing images and, secondly, generating new images from scratch utilizing the Discriminator's features. This approach diverges from traditional uses of discriminators and presents a non-trivial task, considering that features deemed by the network as indicative of 'real' or 'fake' data do not necessarily align with human intuition or aesthetics. Nonetheless, these features can manifest compelling artistic outputs.

Experimental Setup

Experimentation involved a variety of pre-trained models within the StyleGAN2 and StyleGAN3 frameworks, tapping into a diverse array of datasets, including FFHQ, MetFaces, and domain-specific sets like Minecraft imagery and Guatemalan and Mexican huipiles. The authors present a preliminary yet detailed exploration of the Discriminator's layers to derive the "Discriminator Dreaming" results.

Observations and Limitations

The investigation highlighted that leveraging intermediate layers of the Discriminator yields varied artistic results. Video results were also explored, iteratively applying transformations (such as zoom and rotation) to a static image over time. Despite the creative outputs, the algorithm's significant computational overhead remains a limitation, chiefly due to its iterative image synthesis nature, impacting energy efficiency compared to traditional Generators.

Furthermore, the algorithm's exploratory nature necessitates extensive parameter tuning to achieve desired outputs, as different layers and configurations influence the final aesthetic significantly.

Implications and Future Directions

This research opens avenues for utilizing previously sidelined GAN components, specifically Discriminators, thereby promoting more resource-effective deep learning practices, by avoiding the discarding of trained networks. The implications predominantly reside in computational art and creative industries, presenting new opportunities for leveraging AI in artistic expression.

Theoretically, this could inform future GAN models, suggesting a dual role for discriminators in generative tasks. Future work could involve refining the computational efficiency of "Discriminator Dreaming," exploring diverse datasets to broaden artistic output scope, and potentially integrating similar methodologies within other neural network architectures.

In summary, this study provides a compelling argument for the reevaluation of the Discriminator's role within GAN frameworks, proposing its use as an artistic tool through "Discriminator Dreaming." By doing so, it not only enhances the utility of existing AI models but also paves the way for new innovations in the digital art landscape.