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FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE

ICLR 2019 • Masanori Yamada • Kim Heecheol • Kosuke Miyoshi • Hiroshi Yamakawa

A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data... (read more)

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AutoEncoder
Generative Models
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