  We introduce a new unsupervised representation learning and visualization
using deep convolutional networks and self organizing maps called Deep Neural
Maps (DNM). DNM jointly learns an embedding of the input data and a mapping
from the embedding space to a two-dimensional lattice. We compare
visualizations of DNM with those of t-SNE and LLE on the MNIST and COIL-20 data
sets. Our experiments show that the DNM can learn efficient representations of
the input data, which reflects characteristics of each class. This is shown via
back-projecting the neurons of the map on the data space.
