The paper "Analyzing and Improving the Image Quality of StyleGAN" by Tero Karras et al. addresses several artifacts and issues in the StyleGAN architecture, a state-of-the-art generative model for unconditional image synthesis. The authors propose several architectural and training method improvements to enhance image quality and address existing artifacts. Key contributions include: 1. **Generator Normalization**: The authors redesign the generator normalization to remove blob-like artifacts, particularly water droplet-like artifacts that appear in intermediate feature maps around 64x64 resolution. They introduce a "demodulation" operation that replaces instance normalization, improving image quality while retaining full controllability. 2. **Progressive Growing**: They revisit the progressive growing technique, which has been successful in stabilizing high-resolution GAN training but introduces artifacts. The authors propose an alternative design that achieves the same goal without changing the network topology, improving the effective resolution of generated images. 3. **Path Length Regularization**: They introduce a path length regularizer to encourage a smoother mapping from latent space to image space, improving image quality and making the generator easier to invert. This regularizer is computed less frequently to balance computational cost. 4. **Network Architecture**: The authors evaluate different generator and discriminator architectures, finding that skip connections in the generator and residual connections in the discriminator improve performance. They also identify a capacity problem in the generator, which is addressed by increasing the number of feature maps in the highest-resolution layers. 5. **Image Quality and Attribution**: The improved generator makes it easier to attribute generated images to their source, enhancing the practical application of StyleGAN in tasks such as image manipulation and forensic detection. Overall, these improvements redefine the state of the art in unconditional image modeling, both in terms of distribution quality metrics and perceived image quality. The paper provides detailed analysis, experimental results, and implementation details to support these claims.The paper "Analyzing and Improving the Image Quality of StyleGAN" by Tero Karras et al. addresses several artifacts and issues in the StyleGAN architecture, a state-of-the-art generative model for unconditional image synthesis. The authors propose several architectural and training method improvements to enhance image quality and address existing artifacts. Key contributions include: 1. **Generator Normalization**: The authors redesign the generator normalization to remove blob-like artifacts, particularly water droplet-like artifacts that appear in intermediate feature maps around 64x64 resolution. They introduce a "demodulation" operation that replaces instance normalization, improving image quality while retaining full controllability. 2. **Progressive Growing**: They revisit the progressive growing technique, which has been successful in stabilizing high-resolution GAN training but introduces artifacts. The authors propose an alternative design that achieves the same goal without changing the network topology, improving the effective resolution of generated images. 3. **Path Length Regularization**: They introduce a path length regularizer to encourage a smoother mapping from latent space to image space, improving image quality and making the generator easier to invert. This regularizer is computed less frequently to balance computational cost. 4. **Network Architecture**: The authors evaluate different generator and discriminator architectures, finding that skip connections in the generator and residual connections in the discriminator improve performance. They also identify a capacity problem in the generator, which is addressed by increasing the number of feature maps in the highest-resolution layers. 5. **Image Quality and Attribution**: The improved generator makes it easier to attribute generated images to their source, enhancing the practical application of StyleGAN in tasks such as image manipulation and forensic detection. Overall, these improvements redefine the state of the art in unconditional image modeling, both in terms of distribution quality metrics and perceived image quality. The paper provides detailed analysis, experimental results, and implementation details to support these claims.