Posterior Collapse

AbstractVariational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. However, previous works typically focus on synthesizing relatively short sentences (up to …...

AbstractVariational Autoencoder (VAE) is widely used as a generative model to approximate a model’s posterior on latent variables by combining the amortized variational inference and deep neural networks. However, when …...

AbstractVariational autoencoders (VAEs) combine latent variables with amortized variational inference, whose optimization usually converges into a trivial local optimum termed posterior collapse, especially in text modeling. By tracking the optimization …...