Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

NeurIPS 2017 Wei-Ning HsuYu ZhangJames Glass

We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables... (read more)

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