Disentangling data into interpretable and independent factors is critical for controllable generation tasks. With the availability of labeled data, supervision can help enforce the separation of specific factors as expected. However, it is often expensive or even impossible to label every single factor to achieve fully-supervised disentanglement. In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor. Under this setting, we propose a flexible weakly-supervised multi-factor disentanglement framework DisUnknown, which Distills Unknown factors for enabling multi-conditional generation regarding both labeled and unknown factors. Specifically, a two-stage training approach is adopted to first disentangle the unknown factor with an effective and robust training method, and then train the final generator with the proper disentanglement of all labeled factors utilizing the unknown distillation. To demonstrate the generalization capacity and scalability of our method, we evaluate it on multiple benchmark datasets qualitatively and quantitatively and further apply it to various real-world applications on complicated datasets.

本论文提出了一个灵活的弱监督多因素解缕（DisUnknown）框架，通过数据解缕使得已标记和未知因素均可被条件生成，并采用一个分两阶段的训练方法来解缕未知因素，并使用未知因素蒸馏来训练最终的生成器。我们在多个基准数据集上进行了定性和定量评估，并进一步将其应用于各种复杂数据集上的实际应用。

DisUnknown: 用于解缠绕学习的未知因素蒸馏