This paper presents the development of several models of a deep convolutional auto-
encoder in the Caffe deep learning framework and their experimental evaluation on the
example of MNIST dataset. We have created five models of a convolutional auto-encoder
which differ architecturally by the presence or absence of pooling and unpooling layers in
the auto-encoder's encoder and decoder parts. Our results show that the developed models
provide very good results in dimensionality reduction and unsupervised clustering tasks …