  Disentanglement is a highly desirable property of representation due to its
similarity with human understanding and reasoning. However, the performance of
current disentanglement approaches is still unreliable and largely depends on
the hyperparameter selection. Inspired by fractional distillation in chemistry,
we propose DEFT, a disentanglement framework, to raise the lower limit of
disentanglement approaches based on variational autoencoder. It applies a
multi-stage training strategy, including multi-group encoders with different
learning rates and piecewise disentanglement pressure, to stage by stage
distill entangled factors. Furthermore, we provide insight into identifying the
hyperparameters according to the information thresholds. We evaluate DEFT on
three variants of dSprite and SmallNORB, showing robust and high-level
disentanglement scores.
