Learning disentangled representations with semi-supervised deep generative models
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects...
| Main Authors: | , , , , , , , |
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| Format: | Conference item |
| Published: |
Curran Associates
2018
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