  The notion of disentangled autoencoders was proposed as an extension to the
variational autoencoder by introducing a disentanglement parameter $\beta$,
controlling the learning pressure put on the possible underlying latent
representations. For certain values of $\beta$ this kind of autoencoders is
capable of encoding independent input generative factors in separate elements
of the code, leading to a more interpretable and predictable model behaviour.
In this paper we quantify the effects of the parameter $\beta$ on the model
performance and disentanglement. After training multiple models with the same
value of $\beta$, we establish the existence of consistent variance in one of
the disentanglement measures, proposed in literature. The negative consequences
of the disentanglement to the autoencoder's discriminative ability are also
asserted while varying the amount of examples available during training.
