  The key idea behind the unsupervised learning of 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 more than 12000 models
covering most prominent methods and evaluation metrics in a reproducible
large-scale experimental study on seven different 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, increased disentanglement does not
seem to 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.
