The idea behind the \emph{unsupervised} learning of \emph{disentangled}
representations is that real-world data is generated by a few explanatory
factors of variation which can be recovered by unsupervised learning
algorithms. In this paper, we provide a sober look at recent progress in the
field and challenge some common assumptions. We first theoretically show that
the unsupervised learning of disentangled representations is fundamentally
impossible without inductive biases on both the models and the data. Then, we
train over $14000$ models covering most prominent methods and evaluation
metrics in a reproducible large-scale experimental study on eight data sets. We
observe that while the different methods successfully enforce properties
"encouraged" by the corresponding losses, well-disentangled models seemingly
cannot be identified without supervision. Furthermore, different evaluation
metrics do not always agree on what should be considered "disentangled" and
exhibit systematic differences in the estimation. Finally, increased
disentanglement does not seem to necessarily lead to a decreased sample
complexity of learning for downstream tasks. Our results suggest that future
work on disentanglement learning should be explicit about the role of inductive
biases and (implicit) supervision, investigate concrete benefits of enforcing
disentanglement of the learned representations, and consider a reproducible
experimental setup covering several data sets.

本文提供了对无监督学习解开重要变动因素的最新发展的观点，旨在挑战一些常见的假设。本文首先从理论上证明了，无监督的解开表示恒妄图反演和数据上的约定性偏见是不可能的。接着，通过对 8 个数据集进行超过 14,000 个模型的训练，本文发现虽然不同的方法成功地实现了相应损失所鼓励的特性，但是没有监督的情况下，好的解开模型似乎无法被识别。此外，不同的评估指标并不总是在什么应该被认为是 “解开的” 上达成一致，而且在估计上表现出系统性差异。最后，增加的解开性似乎并不一定会导致学习下游任务的样本复杂度减少。本文的结果表明，关于解开学习的未来工作应该明确归因于诱导偏见和（隐含的）监督的作用，研究解开表示的具体好处，并考虑到涵盖几个数据集的可重复的实验设置。