  Constructing disentangled representations is known to be a difficult task,
especially in the unsupervised scenario. The dominating paradigm of
unsupervised disentanglement is currently to train a generative model that
separates different factors of variation in its latent space. This separation
is typically enforced by training with specific regularization terms in the
model's objective function. These terms, however, introduce additional
hyperparameters responsible for the trade-off between disentanglement and
generation quality. While tuning these hyperparameters is crucial for proper
disentanglement, it is often unclear how to tune them without external
supervision.
  This paper investigates an alternative route to disentangled representations.
Namely, we propose to extract such representations from the state-of-the-art
generative models trained without disentangling terms in their objectives. This
paradigm of post hoc disentanglement employs little or no hyperparameters when
learning representations while achieving results on par with existing
state-of-the-art, as shown by comparison in terms of established
disentanglement metrics, fairness, and the abstract reasoning task. All our
code and models are publicly available.
