Disentangled representations have recently been shown to improve data efficiency, generalisation, robustness and interpretability in simple supervised and reinforcement learning tasks. To extend such results to more complex domains, it is important to address a major shortcoming of the current state of the art unsupervised disentangling approaches -- high convergence variance, whereby different disentanglement quality may be achieved by the same model depending on its initial state. The existing model selection methods require access to the ground truth attribute labels, which are not available for most datasets. Hence, the benefits of disentangled representations have not yet been fully explored in practical applications. This paper addresses this problem by introducing a simple yet robust and reliable method for unsupervised disentangled model selection. We show that our approach performs comparably to the existing supervised alternatives across 5400 models from six state of the art unsupervised disentangled representation learning model classes.

本论文介绍了一种简单而健壮的无监督分解模型选择方法（UDR），通过对训练模型表示进行成对比较来量化分离质量，该方法能够使超参数调整和模型选择更加有效，并且与现有的有监督替代方案表现相当。同时，我们还发现，这种排序方法与两个不同领域的最终任务表现有很好的相关性。

变分解耦表征学习无监督模型选择