NoisyTwins: Class-Consistent and Diverse Image Generation Through StyleGANs
NoisyTwins: Class-Consistent and Diverse Image Generation Through StyleGANs
StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulation. However, the performance of StyleGANs severely degrades when trained via class-conditioning on large-scale long-tailed datasets. We find that one reason for degradation is …