In this post, I’m going to implement a text Variational Auto Encoder (VAE), inspired to the paper “Generating sentences from a continuous space”, in Keras. First, I’ll briefly introduce generative models, the VAE, its characteristics and its advantages; then I’ll show the code to implement the text VAE in keras and finally I will ...

Mar 17, 2019 · We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Gaussian prior for the latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module.

Apr 28, 2025 · We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module.

Feb 8, 2017 · In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both...

Recurrent neural network language(RNNLM) is powerful and scalable for text generation in unsupervised generative modeling. We extended the RNNLM and propose the Variational Auto-Encoder Recurrent Neural Network(VAE-RNNLM), which designed to explicitly capture such global features as continuous latent variable.

Apr 28, 2025 · In this paper we explore the effect of architectural choices on learning a variational autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components ...

Text generation with a Variational Autoencoder in Keras. In this tutorial we will try to generate text with a variational autoencoder and interpolate between sentences like in the paper "Generating sentences from a continuous space" https://arxiv.org/abs/1511.06349

Experimental results show that our TGVAE outperforms alterna-tive approaches on both unconditional and con-ditional text generation, which can generate semantically-meaningful sentences with vari-ous topics.

Sep 27, 2018 · In this paper, we propose a novel framework, hierarchically-structured variational autoencoder (hier-VAE), for generating long and coherent units of text. To enhance the model’s plan-ahead ability, intermediate sentence representations are introduced into the generative networks to guide the word-level predictions.

Jul 13, 2019 · It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problems. The model would collapse into a plain language model that totally ignores the latent variables and can only generate repeating and dull samples.