Golden
Variational autoencoder

Variational autoencoder

Type of neural network that reconstruct output from input and consist of an encoder and a decoder

Variational autoencoder (VAE), one of the approaches to unsupervised learning of complicated distributions. VAEs are built on top of neural networks (standard function approximators). They can be trained with stochastic gradient descent. Consist of an encoder and a decoder, which are encoding and decoding the data.



VAEs have shown results in generating many kinds of complicated data, including handwritten digits, faces, house numbers, images, physical models of scenes, segmentation and predicting the future from static images.

Timeline

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Further reading

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Auto-Encoding Variational Bayes

Diederik P Kingma, Max Welling

Academic paper

December 20, 2013

Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders

Xiaopeng Yang, Xiaowen Lin, Shunda Suo, Ming Li

Academic paper



GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures

Gaëtan Hadjeres, Frank Nielsen, François Pachet

Academic paper



InfoVAE: Information Maximizing Variational Autoencoders

Shengjia Zhao, Jiaming Song, Stefano Ermon

Academic paper



Isolating Sources of Disentanglement in Variational Autoencoders

Tian Qi Chen, Xuechen Li, Roger Grosse, David Duvenaud

Academic paper



Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

Tiancheng Zhao, Ran Zhao, Maxine Eskenazi

Academic paper



Tutorial on Variational Autoencoders

Carl Doersch

Academic paper



TVAE: Triplet-Based Variational Autoencoder using Metric Learning

Haque Ishfaq, Assaf Hoogi, Daniel Rubin

Academic paper



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