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Zhao, S., Song, J. and Ermon, S. (2017) Learning Hierarchical Features from Generative Models.
http://arxiv.org/abs/1702.08396

has been cited by the following article:

• TITLE: Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)

  AUTHORS: Haoji Xu

  KEYWORDS: Generative Models, Ladder Variational Autoencoders, Facial Recognition

  JOURNAL NAME: Intelligent Information Management, Vol.10 No.4, July 17, 2018

  ABSTRACT: Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, while extracting natural and human-understandable features without labels. In this paper we combine two highly useful classes of models, variational ladder autoencoders, and MMD variational autoencoders, to model face images. In particular, we show that we can disentangle highly meaningful and interpretable features. Furthermore, we are able to perform arithmetic operations on faces and modify faces to add or remove high level features.