Feature Proliferation -- the "Cancer" in StyleGAN and its Treatments
Shuang Song Yuanbang Liang Jing Wu Yu-Kun Lai Yipeng Qin
Despite the success of StyleGAN in image synthesis, the images it synthesizes
are not always perfect and the well-known truncation trick has become a
standard post-processing technique for StyleGAN to synthesize high-quality
images. Although effective, it has long been noted that the truncation trick
tends to reduce the diversity of synthesized images and unnecessarily
sacrifices many distinct image features. To address this issue, in this paper,
we first delve into the StyleGAN image synthesis mechanism and discover an
important phenomenon, namely Feature Proliferation, which demonstrates how
specific features reproduce with forward propagation. Then, we show how the
occurrence of Feature Proliferation results in StyleGAN image artifacts. As an
analogy, we refer to it as the" cancer" in StyleGAN from its proliferating and
malignant nature. Finally, we propose a novel feature rescaling method that
identifies and modulates risky features to mitigate feature proliferation.
Thanks to our discovery of Feature Proliferation, the proposed feature
rescaling method is less destructive and retains more useful image features
than the truncation trick, as it is more fine-grained and works in a
lower-level feature space rather than a high-level latent space. Experimental
results justify the validity of our claims and the effectiveness of the
proposed feature rescaling method. Our code is available at https://github.
com/songc42/Feature-proliferation.