Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a very limited amount of supervision, for example through manual labeling of training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 29000 models under well-defined and reproducible experimental conditions. We first observe that a very limited number of labeled examples (0.01--0.5% of the data set) is sufficient to perform model selection on state-of-the-art unsupervised models. Yet, if one has access to labels for supervised model selection, this raises the natural question of whether they should also be incorporated into the training process. As a case-study, we test the benefit of introducing (very limited) supervision into existing state-of-the-art unsupervised disentanglement methods exploiting both the values of the labels and the ordinal information that can be deduced from them. Overall, we empirically validate that with very little and potentially imprecise supervision it is possible to reliably learn disentangled representations.

针对学习解耦表示是表示学习中重要的问题，本文调查研究了少量监督如何影响现有的解耦学习方法，并进行了大规模实验，结果表明，即使标签不完全或不准确，使用少量的标记训练样本可以对现有的解耦学习模型进行模型选择，并证实引入监督可以有效地学习解耦表示。

使用少量标签解开变异因素