Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs

Djordje Miladinovic · Kumar Shridhar · Kushal Jain · Max Paulus · Joachim M Buhmann · Carl Allen

Hall J #439

Keywords: [ posterior collapse ] [ Dropout ] [ VAE ]

[ Abstract ]
[ Poster [ OpenReview
Wed 30 Nov 9 a.m. PST — 11 a.m. PST


In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning. However, training sequence VAEs is challenging: autoregressive decoders can often explain the data without utilizing the latent space, known as posterior collapse. To mitigate this, state-of-the-art models weaken' thepowerful decoder' by applying uniformly random dropout to the decoder input.We show theoretically that this removes pointwise mutual information provided by the decoder input, which is compensated for by utilizing the latent space. We then propose an adversarial training strategy to achieve information-based stochastic dropout. Compared to uniform dropout on standard text benchmark datasets, our targeted approach increases both sequence modeling performance and the information captured in the latent space.

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