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arXiv · 2110.14945

Preventing posterior collapse in variational autoencoders for text generation via decoder regularization

Alban Petit·Caio Corro
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Abstract

Variational autoencoders trained to minimize the reconstruction error are sensitive to the posterior collapse problem, that is the proposal posterior distribution is always equal to the prior. We propose a novel regularization method based on fraternal dropout to prevent posterior collapse. We evaluate our approach using several metrics and observe improvements in all the tested configurations.

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Alban Petit, Caio Corro. 2021-10-28. Preventing posterior collapse in variational autoencoders for text generation via decoder regularization. https://arxiv.org/abs/2110.14945

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