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Found 3 papers, 2 papers with code
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching.
Neural Gaussian Copula for Variational Autoencoder
We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models.
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior.
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