  Understanding which inductive biases could be helpful for the unsupervised
learning of object-centric representations of natural scenes is challenging. In
this paper, we systematically investigate the performance of two models on
datasets where neural style transfer was used to obtain objects with complex
textures while still retaining ground-truth annotations. We find that by using
a single module to reconstruct both the shape and visual appearance of each
object, the model learns more useful representations and achieves better object
separation. In addition, we observe that adjusting the latent space size is
insufficient to improve segmentation performance. Finally, the downstream
usefulness of the representations is significantly more strongly correlated
with segmentation quality than with reconstruction accuracy.
