Disentangled Recurrent Wasserstein Autoencoder

Understand

Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework.

• However, only a few works have explored unsupervised disentangled sequential representation learning due to challenges of generating sequential data.
• In this paper, we propose recurrent Wasserstein Autoencoder (R-WAE), a new framework for generative modeling of sequential data.
• R-WAE disentangles the representation of an input sequence into static and dynamic factors (i.e., time-invariant and time-varying parts).