  The development of a deep (stacked) convolutional auto-encoder in the Caffe
deep learning framework is presented in this paper. We describe simple
principles which we used to create this model in Caffe. The proposed model of
convolutional auto-encoder does not have pooling/unpooling layers yet. The
results of our experimental research show comparable accuracy of dimensionality
reduction in comparison with a classic auto-encoder on the example of MNIST
dataset.
