Efficient decomposition of latent representation in generative models

In designing self-organizing generative models of robot behaviour, it is important to address the issue of generalization for multiple patterns, while keeping latent representation in a low dimension. We are investigating the possibility of introducing local coordinates for samples of each pattern in shared latent representation. Moreover, recent advances in efficient, approximate computation of Jacobians allows us to introduce specific regularization that ensures directional robustness in introduced local coordinates.