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.

翻译：最近推出的细小的分解表态提议放松以前关于分解的定义中的一些限制,以换取更大的灵活性;然而,目前,随着数据变化因素的增加,只有增加监督量,才能使分解弱;在本文件中,我们引入了分解弱的模块化表态,这是一种新颖的方法,可以使受监督的信息量与基因变异因素的数量保持一致;实验表明,采用模块化表态的模型可以提高以往工作的绩效,而不需要额外的监督。