Deep Neural Maps

Mehran Pesteie, Purang Abolmaesumi, Robert Rohling

Abstract: We introduce a new unsupervised representation learning and visualization method 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.