  Despite tremendous progress over the past decade, deep learning methods
generally fall short of human-level systematic generalization. It has been
argued that explicitly capturing the underlying structure of data should allow
connectionist systems to generalize in a more predictable and systematic
manner. Indeed, evidence in humans suggests that interpreting the world in
terms of symbol-like compositional entities may be crucial for intelligent
behavior and high-level reasoning. Another common limitation of deep learning
systems is that they require large amounts of training data, which can be
expensive to obtain. In representation learning, large datasets are leveraged
to learn generic data representations that may be useful for efficient learning
of arbitrary downstream tasks.
  This thesis is about structured representation learning. We study methods
that learn, with little or no supervision, representations of unstructured data
that capture its hidden structure. In the first part of the thesis, we focus on
representations that disentangle the explanatory factors of variation of the
data. We scale up disentangled representation learning to a novel robotic
dataset, and perform a systematic large-scale study on the role of pretrained
representations for out-of-distribution generalization in downstream robotic
tasks. The second part of this thesis focuses on object-centric
representations, which capture the compositional structure of the input in
terms of symbol-like entities, such as objects in visual scenes. Object-centric
learning methods learn to form meaningful entities from unstructured input,
enabling symbolic information processing on a connectionist substrate. In this
study, we train a selection of methods on several common datasets, and
investigate their usefulness for downstream tasks and their ability to
generalize out of distribution.
