Ladder variational autoencoders
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Ladder variational autoencoders. / Sønderby, Casper Kaae; Raiko, Tapani; Maaløe, Lars; Sønderby, Søren Kaae; Winther, Ole.
Advances in Neural Information Processing Systems 29 (NIPS 2016). ed. / D. D. Lee; M. Sugiyama; U. V. Luxburg; I. Guyon; R. Garnett. Curran Associates, Inc., 2016. p. 3745-3753 (Advances in Neural Information Processing Systems, Vol. 29).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Ladder variational autoencoders
AU - Sønderby, Casper Kaae
AU - Raiko, Tapani
AU - Maaløe, Lars
AU - Sønderby, Søren Kaae
AU - Winther, Ole
N1 - Conference code: 30
PY - 2016
Y1 - 2016
N2 - Variational autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network. We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models. We provide a detailed analysis of the learned hierarchical latent representation and show that our new inference model is qualitatively different and utilizes a deeper more distributed hierarchy of latent variables. Finally, we observe that batch-normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers.
AB - Variational autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network. We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models. We provide a detailed analysis of the learned hierarchical latent representation and show that our new inference model is qualitatively different and utilizes a deeper more distributed hierarchy of latent variables. Finally, we observe that batch-normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers.
UR - http://www.scopus.com/inward/record.url?scp=85019264158&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85019264158
T3 - Advances in Neural Information Processing Systems
SP - 3745
EP - 3753
BT - Advances in Neural Information Processing Systems 29 (NIPS 2016)
A2 - Lee, D. D.
A2 - Sugiyama, M.
A2 - Luxburg, U. V.
A2 - Guyon, I.
A2 - Garnett, R.
PB - Curran Associates, Inc.
Y2 - 5 December 2016 through 10 December 2016
ER -
ID: 179322079