Reviews: Variational Autoencoder for Deep Learning of Images, Labels and Captions

This paper presents a model and method which are likely to be of interest to many in the community. The formulation allows stochastic layers of a convolution-deconvolution structured autoencoder with stochastic layers to be parameterized and manipulated in such a way that inference can be performed in a much more computationally efficient way compared to Gibbs sampling and MCEM techniques. Results on CIFAR are fairly far from state of the art, but illustrate the key contributions related to efficient inference well. Results for a few other methods would be useful to include in Table 1 to give the reader some context as to what regime this approach and the examined model are operating within. The Flickr8k, 30k and MS COCO results seem quite strong, especially given that this work does not focus on the application, but rather the proposed VAE method.