Inductive Biases for Object-Centric Representations of Complex Textures

Understanding which inductive biases could be useful for the unsupervised learning of object-centric representations of natural scenes is challenging. Here, we use neural style transfer to generate datasets where objects have complex textures while still retaining ground-truth annotations. We find that, when a model effectively balances the importance of shape and appearance in the training objective, it can achieve better separation of the objects and learn more useful object representations.