  The use of well-disentangled representations offers many advantages for
downstream tasks, e.g. an increased sample efficiency, or better
interpretability. However, the quality of disentangled interpretations is often
highly dependent on the choice of dataset-specific hyperparameters, in
particular the regularization strength. To address this issue, we introduce
DAVA, a novel training procedure for variational auto-encoders. DAVA completely
alleviates the problem of hyperparameter selection. We compare DAVA to models
with optimal hyperparameters. Without any hyperparameter tuning, DAVA is
competitive on a diverse range of commonly used datasets. Underlying DAVA, we
discover a necessary condition for unsupervised disentanglement, which we call
PIPE. We demonstrate the ability of PIPE to positively predict the performance
of downstream models in abstract reasoning. We also thoroughly investigate
correlations with existing supervised and unsupervised metrics. The code is
available at https://github.com/besterma/dava.
