DGL-GAN: Discriminator Guided GAN Compression

Core Concepts

Utilizing the teacher discriminator in DGL-GAN significantly improves the performance of compressed and uncompressed GANs.

Abstract

The content introduces DGL-GAN, a novel approach for compressing vanilla GANs by leveraging the knowledge from the teacher discriminator. It discusses the challenges in compressing large-scale GANs like StyleGAN2 and BigGAN, highlighting the importance of reducing computation costs while maintaining image quality. The two-stage training strategy of DGL-GAN is explained, showing how it stabilizes optimization and boosts performance. Results demonstrate that DGL-GAN achieves state-of-the-art results on both StyleGAN2 and BigGAN, even surpassing original models in some cases. A comprehensive ablation study validates the effectiveness of DGL-GAN, showcasing its superiority over other compression methods.
Introduction

Generative Adversarial Networks (GANs) have revolutionized computer vision tasks.
Compressing large-scale GANs is challenging due to computational limitations.
Existing compression techniques focus on conditional GANs, with limited solutions for vanilla GANs.
Methodology

DGL-GAN proposes a Discriminator Guided Learning approach for compressing vanilla GANs.
It transfers knowledge from the teacher discriminator to improve student generator performance.
Two-stage training stabilizes optimization and enhances results on StyleGAN2 and BigGAN.
Results

DGL-GAN achieves state-of-the-art results on both StyleGAN2 and BigGAN.
Compressed models show comparable or improved performance compared to original models.
Uncompressed DGL-GAN outperforms StyleGAN2, demonstrating its effectiveness in boosting performance.
Conclusion
DGL-GAN proves to be an effective method for compressing and enhancing the performance of vanilla GANs through teacher discriminator guidance.

Stats

Experiments show that DGL-GAN achieves FID 2.65 on FFHQ with compressed StyleGAN2.

Quotes

"The teacher discriminator may contain more meaningful information than the student discriminator."
"DGL-GAN outperforms existing compression methods with lower computation complexity."

Key Insights Distilled From

DGL-GAN

by Yuesong Tian... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2112.06502.pdf

Deeper Inquiries

How can DGL-GAN be applied to other types of generative models

DGL-GAN can be applied to other types of generative models by adapting the concept of transferring knowledge from a teacher discriminator to a student generator. This approach can be implemented in various generative models, such as Variational Autoencoders (VAEs), Autoregressive Models, and Flow-based Models. By training the student generator with guidance from the teacher discriminator, these models can benefit from improved performance and efficiency. Additionally, DGL-GAN's two-stage training strategy can be utilized in different generative models to stabilize the optimization process and enhance overall results.

What are potential drawbacks or limitations of relying heavily on the teacher discriminator

One potential drawback of relying heavily on the teacher discriminator is that it may limit the diversity or creativity of generated outputs. Since the teacher discriminator provides supervision based on its learned distribution, there is a risk of bias towards specific patterns or features present in the original data. This could result in a lack of exploration in generating novel or diverse samples. Furthermore, if the teacher discriminator has limitations or biases inherent in its training data, those constraints may transfer to the student generator through DGL-GAN.

How might incorporating domain-specific knowledge impact the performance of compressed GANS

Incorporating domain-specific knowledge into compressed GANs could significantly impact their performance by enhancing output quality and relevance within that particular domain. By leveraging domain expertise during compression using techniques like DGL-GAN, GANs can learn more relevant features and generate outputs tailored to specific applications or industries. This targeted approach can lead to better image synthesis results that align closely with domain requirements and standards.