Variational Autoencoder for Deep Learning of Images, Labels and Captions
Abstract
A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of the latent image features, and a deep Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used to approximate a distribution for the latent DGDN features/code. The latent code is also linked to generative models for labels (Bayesian support vector machine) or captions (recurrent neural network). When predicting a label/caption for a new image at test, averaging is performed across the distribution of latent codes; this is computationally efficient as a consequence of the learned CNN-based encoder. Since the framework is capable of modeling the image in the presence/absence of associated labels/captions, a new semi-supervised setting is manifested for CNN learning with images; the framework even allows unsupervised CNN learning, based on images alone.
Cite
@article{arxiv.1609.08976,
title = {Variational Autoencoder for Deep Learning of Images, Labels and Captions},
author = {Yunchen Pu and Zhe Gan and Ricardo Henao and Xin Yuan and Chunyuan Li and Andrew Stevens and Lawrence Carin},
journal= {arXiv preprint arXiv:1609.08976},
year = {2016}
}
Comments
NIPS 2016 (To appear)