Rate-Invariant Autoencoding of Time-Series

For time-series classification and retrieval applications, an important requirement is to develop representations/metrics that are robust to re-parametrization of the time-axis. Temporal re-parametrization as a model can account for variability in the underlying generative process, sampling rate variations, or plain temporal mis-alignment. In this paper, we extend prior work in disentangling latent spaces of autoencoding models, to design a novel architecture to learn rate-invariant latent codes in a completely unsupervised fashion. Unlike conventional neural network architectures, this method allows to explicitly disentangle temporal parameters in the form of order-preserving diffeomorphisms with respect to a learnable template. This makes the latent space more easily interpretable. We show the efficacy of our approach on a synthetic dataset and a real dataset for hand action-recognition.