This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional probabilities in original and new distributions. We approximate the difference with models' generalization abilities so that it fits in the standard machine learning framework and can be efficiently computed. In contrast to the state-of-the-art approach, which relies on the learner's adaptation speed to new distribution, the proposed approach only requires evaluating the model's generalization ability. We provide a theoretical explanation for the advantage of the proposed method, and our experiments show that the proposed technique is 1.9--11.0$\times$ more sample efficient and 9.4--32.4 times quicker than the previous method on various tasks. The source code is available at \url{https://github.com/yuanpeng16/EDCR}.

翻译：本文提出了一种基于原始分布与新分布条件概率差异的高效解耦表征学习方法，该方法通过模型的泛化能力近似该差异，从而适配标准机器学习框架并实现高效计算。与依赖学习者对新分布适应速度的现有最优方法不同，本方法仅需评估模型的泛化能力。我们从理论上解释了所提方法的优势，实验表明，该技术在多种任务上的样本效率提升1.9–11.0倍，计算速度提升9.4–32.4倍。源代码见\url{https://github.com/yuanpeng16/EDCR}。