However, G-CNNs are faced withtwomajorchallenges: spatial-agnosticproblem andexpensivecomputational cost. However, it is essentially G-CNNs which still have the inherent spatial-agnostic problem.

In this study, we present \textit{meta-sequential prediction} (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three.

In this study, we present \textit{meta-sequential prediction} (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. We will demonstrate that, with our framework, the hidden disentangled structure of the dataset naturally emerges as a by-product by applying \textit{simultaneous block-diagonalization} to the transition operators in the latent space, the procedure which is commonly used in representation theory to decompose the feature-space based on the type of response to group actions.We will showcase our method from both empirical and theoretical perspectives.Our result suggests that finding a simple structured relation and learning a model with extrapolation capability are two sides of the same coin.