  The idea behind the \emph{unsupervised} learning of \emph{disentangled}
representations is that real-world data is generated by a few explanatory
factors of variation which can be recovered by unsupervised learning
algorithms. In this paper, we provide a sober look at recent progress in the
field and challenge some common assumptions. We first theoretically show that
the unsupervised learning of disentangled representations is fundamentally
impossible without inductive biases on both the models and the data. Then, we
train over $14000$ models covering most prominent methods and evaluation
metrics in a reproducible large-scale experimental study on eight data sets. We
observe that while the different methods successfully enforce properties
"encouraged" by the corresponding losses, well-disentangled models seemingly
cannot be identified without supervision. Furthermore, different evaluation
metrics do not always agree on what should be considered "disentangled" and
exhibit systematic differences in the estimation. Finally, increased
disentanglement does not seem to necessarily lead to a decreased sample
complexity of learning for downstream tasks. Our results suggest that future
work on disentanglement learning should be explicit about the role of inductive
biases and (implicit) supervision, investigate concrete benefits of enforcing
disentanglement of the learned representations, and consider a reproducible
experimental setup covering several data sets.
