  Disentanglement learning is crucial for obtaining disentangled
representations and controllable generation. Current disentanglement methods
face several inherent limitations: difficulty with high-resolution images,
primarily focusing on learning disentangled representations, and
non-identifiability due to the unsupervised setting. To alleviate these
limitations, we design new architectures and loss functions based on StyleGAN
(Karras et al., 2019), for semi-supervised high-resolution disentanglement
learning. We create two complex high-resolution synthetic datasets for
systematic testing. We investigate the impact of limited supervision and find
that using only 0.25%~2.5% of labeled data is sufficient for good
disentanglement on both synthetic and real datasets. We propose new metrics to
quantify generator controllability, and observe there may exist a crucial
trade-off between disentangled representation learning and controllable
generation. We also consider semantic fine-grained image editing to achieve
better generalization to unseen images.
