Self-supervised representation learning often uses data augmentations to
induce some invariance to "style" attributes of the data. However, with
downstream tasks generally unknown at training time, it is difficult to deduce
a priori which attributes of the data are indeed "style" and can be safely
discarded. To address this, we introduce a more principled approach that seeks
to disentangle style features rather than discard them. The key idea is to add
multiple style embedding spaces where: (i) each is invariant to all-but-one
augmentation; and (ii) joint entropy is maximized. We formalize our structured
data-augmentation procedure from a causal latent-variable-model perspective,
and prove identifiability of both content and (multiple blocks of) style
variables. We empirically demonstrate the benefits of our approach on synthetic
datasets and then present promising but limited results on ImageNet.

自我监督表示学习经常使用数据增强来诱导对数据的 “风格” 属性的某种不变性。然而，由于在训练时通常不知道下游任务，很难事先推断哪些属性实际上是 “风格”，并且可以安全地丢弃。为了解决这个问题，我们引入了一种更有原则性的方法，旨在解开 “风格” 特征而不是丢弃它们。关键思想是添加多个风格嵌入空间，其中：（i）每个空间对除一个增强操作外都不变；（ii）联合熵被最大化。我们从因果潜变量模型的角度形式化了我们的结构化数据增强过程，并证明了内容和（多个模块的）风格变量的可辨识性。我们在合成数据集上通过实验证明了我们方法的好处，然后在 ImageNet 上展示了有限但有希望的结果。