论文标题
关于无监督的分解表示的评论
A Commentary on the Unsupervised Learning of Disentangled Representations
论文作者
论文摘要
无监督的分解表示形式学习的目的是将数据差异的独立解释因素分开，而无需访问监督。在本文中，我们总结了Locatello等人，2019年的结果，并着重于他们对从业者的影响。我们讨论了理论上的结果表明，如果没有归纳偏见及其所带来的实际挑战，从根本上来说，无监督的对分解表示的学习是不可能的。最后，我们评论了我们的实验发现，强调了最先进的方法和未来研究方向的局限性。
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research.
