Khái niệm cốt lõi
Utilizing the teacher discriminator in DGL-GAN significantly improves the performance of compressed and uncompressed GANs.
Tóm tắt
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.
Thống kê
Experiments show that DGL-GAN achieves FID 2.65 on FFHQ with compressed StyleGAN2.
Trích dẫn
"The teacher discriminator may contain more meaningful information than the student discriminator."
"DGL-GAN outperforms existing compression methods with lower computation complexity."