The process of generating data such as images is controlled by independent and unknown factors of variation. The retrieval of these variables has been studied extensively in the disentanglement, causal representation learning, and independent component analysis fields. Recently, approaches merging these domains together have shown great success. Instead of directly representing the factors of variation, the problem of disentanglement can be seen as finding the interventions on one image that yield a change to a single factor. Following this assumption, we introduce a new method for disentanglement inspired by causal dynamics that combines causality theory with vector-quantized variational autoencoders. Our model considers the quantized vectors as causal variables and links them in a causal graph. It performs causal interventions on the graph and generates atomic transitions affecting a unique factor of variation in the image. We also introduce a new task of action retrieval that consists of finding the action responsible for the transition between two images. We test our method on standard synthetic and real-world disentanglement datasets. We show that it can effectively disentangle the factors of variation and perform precise interventions on high-level semantic attributes of an image without affecting its quality, even with imbalanced data distributions.

翻译：数据生成过程（如图像生成）受独立且未知的变异因素控制。解耦、因果表征学习以及独立成分分析领域已对这类变量的提取进行了广泛研究。近年来，融合上述领域的方法取得了显著成功。解耦问题并非直接表征变异因素，而是可以视为寻找使单一因素发生变化的图像干预操作。基于这一假设，我们提出了一种受因果动力学启发的新解耦方法，该方法将因果理论与向量量化变分自编码器相结合。模型将量化向量视为因果变量，并通过因果图建立其关联关系。通过在图结构上执行因果干预，模型能够生成仅影响图像中单一变异因素的原子级转变。同时，我们提出了动作检索这一新任务，旨在识别导致两幅图像间转换的因果动作。在标准合成数据集和真实世界解耦数据集上的实验表明，即使面对不平衡的数据分布，该方法仍能有效解耦变异因素，并在不影响图像质量的前提下，对图像的高层语义属性执行精确干预。