Papers with Wasserstein objective
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling (N19-1)
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| Challenge: | Empirical experiments show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse. |
| Approach: | They propose a Variational Wasserstein Autoencoder with Riemannian Normalizing Flow to solve this problem by transforming a latent variable into a space that respects the geometric characteristics of input space. |
| Outcome: | Empirical results show that the proposed model avoids KLvanishing and has better performance in language modeling, likelihood approximation, and text generation tasks. |