  Content and style (C-S) disentanglement intends to decompose the underlying
explanatory factors of objects into two independent subspaces. From the
unsupervised disentanglement perspective, we rethink content and style and
propose a formulation for unsupervised C-S disentanglement based on our
assumption that different factors are of different importance and popularity
for image reconstruction, which serves as a data bias. The corresponding model
inductive bias is introduced by our proposed C-S disentanglement Module (C-S
DisMo), which assigns different and independent roles to content and style when
approximating the real data distributions. Specifically, each content embedding
from the dataset, which encodes the most dominant factors for image
reconstruction, is assumed to be sampled from a shared distribution across the
dataset. The style embedding for a particular image, encoding the remaining
factors, is used to customize the shared distribution through an affine
transformation. The experiments on several popular datasets demonstrate that
our method achieves the state-of-the-art unsupervised C-S disentanglement,
which is comparable or even better than supervised methods. We verify the
effectiveness of our method by downstream tasks: domain translation and
single-view 3D reconstruction. Project page at
https://github.com/xrenaa/CS-DisMo.
