  Traditionally, when generative models of data are developed via deep
architectures, greedy layer-wise pre-training is employed. In a well-trained
model, the lower layer of the architecture models the data distribution
conditional upon the hidden variables, while the higher layers model the hidden
distribution prior. But due to the greedy scheme of the layerwise training
technique, the parameters of lower layers are fixed when training higher
layers. This makes it extremely challenging for the model to learn the hidden
distribution prior, which in turn leads to a suboptimal model for the data
distribution. We therefore investigate joint training of deep autoencoders,
where the architecture is viewed as one stack of two or more single-layer
autoencoders. A single global reconstruction objective is jointly optimized,
such that the objective for the single autoencoders at each layer acts as a
local, layer-level regularizer. We empirically evaluate the performance of this
joint training scheme and observe that it not only learns a better data model,
but also learns better higher layer representations, which highlights its
potential for unsupervised feature learning. In addition, we find that the
usage of regularizations in the joint training scheme is crucial in achieving
good performance. In the supervised setting, joint training also shows superior
performance when training deeper models. The joint training framework can thus
provide a platform for investigating more efficient usage of different types of
regularizers, especially in light of the growing volumes of available unlabeled
data.
