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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.

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Carl Doersch

Tutorial on Variational Autoencoders

Academic paper

Gaëtan Hadjeres, Frank Nielsen, François Pachet

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

Academic paper

Haque Ishfaq, Assaf Hoogi, Daniel Rubin

TVAE: Triplet-Based Variational Autoencoder using Metric Learning

Academic paper

Shengjia Zhao, Jiaming Song, Stefano Ermon

InfoVAE: Information Maximizing Variational Autoencoders

Academic paper

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

Isolating Sources of Disentanglement in Variational Autoencoders

Academic paper

Tiancheng Zhao, Ran Zhao, Maxine Eskenazi

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

Academic paper

Xiaopeng Yang, Xiaowen Lin, Shunda Suo, Ming Li

Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders

Academic paper

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