论文标题
模块化表示弱分解的表示
Modular Representations for Weak Disentanglement
论文作者
论文摘要
最近提出的弱分解表示形式，旨在放大先前的解开定义的某些限制，以换取更具灵活性。但是，目前，只有随着数据变化的数量增加，才能通过增加监督数量来实现弱分解。在本文中，我们介绍了弱解释的模块化表示，这是一种新颖的方法，允许与生成因子的数量保持监督信息的量。实验表明，使用模块化表示的模型可以提高其相对于以前的工作的性能，而无需额外监督。
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.
