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.
@article{arxiv.1810.07291,
title = {Deep Neural Maps},
author = {Mehran Pesteie and Purang Abolmaesumi and Robert Rohling},
journal= {arXiv preprint arXiv:1810.07291},
year = {2018}
}