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Variational Autoencoder for Deep Learning of Images, Labels and Captions

Yunchen Pu · Zhe Gan · Ricardo Henao · Xin Yuan · Chunyuan Li · Andrew Stevens · Lawrence Carin

Area 5+6+7+8 #78

Keywords: [ Semi-Supervised Learning ] [ Variational Inference ] [ Deep Learning or Neural Networks ]


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.

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