  The recently introduced weakly disentangled representations proposed to relax
some constraints of the previous definitions of disentanglement, in exchange
for more flexibility. However, at the moment, weak disentanglement can only be
achieved by increasing the amount of supervision as the number of factors of
variations of the data increase. In this paper, we introduce modular
representations for weak disentanglement, a novel method that allows to keep
the amount of supervised information constant with respect the number of
generative factors. The experiments shows that models using modular
representations can increase their performance with respect to previous work
without the need of additional supervision.
