  Disentanglement is a highly desirable property of representation due to its
similarity with human's understanding and reasoning. This improves
interpretability, enables the performance of down-stream tasks, and enables
controllable generative models. However, this domain is challenged by the
abstract notion and incomplete theories to support unsupervised disentanglement
learning. We demonstrate the data itself, such as the orientation of images,
plays a crucial role in disentanglement and instead of the factors, and the
disentangled representations align the latent variables with the action
sequences. We further introduce the concept of disentangling action sequences
which facilitates the description of the behaviours of the existing
disentangling approaches. An analogy for this process is to discover the
commonality between the things and categorizing them. Furthermore, we analyze
the inductive biases on the data and find that the latent information
thresholds are correlated with the significance of the actions. For the
supervised and unsupervised settings, we respectively introduce two methods to
measure the thresholds. We further propose a novel framework, fractional
variational autoencoder (FVAE), to disentangle the action sequences with
different significance step-by-step. Experimental results on dSprites and 3D
Chairs show that FVAE improves the stability of disentanglement.
