Intelligent Information Management

Volume 10, Issue 4 (July 2018)

ISSN Print: 2160-5912   ISSN Online: 2160-5920

Google-based Impact Factor: 1.70  Citations  h5-index & Ranking

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

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DOI: 10.4236/iim.2018.104009    728 Downloads   1,674 Views   Citations
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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.

Cite this paper

Xu, H. (2018) Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD). Intelligent Information Management, 10, 108-113. doi: 10.4236/iim.2018.104009.

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