  We propose a method to facilitate exploration and analysis of new large data
sets. In particular, we give an unsupervised deep learning approach to learning
a latent representation that captures semantic similarity in the data set. The
core idea is to use data augmentations that preserve semantic meaning to
generate synthetic examples of elements whose feature representations should be
close to one another.
  We demonstrate the utility of our method applied to nano-scale electron
microscopy data, where even relatively small portions of animal brains can
require terabytes of image data. Although supervised methods can be used to
predict and identify known patterns of interest, the scale of the data makes it
difficult to mine and analyze patterns that are not known a priori. We show the
ability of our learned representation to enable query by example, so that if a
scientist notices an interesting pattern in the data, they can be presented
with other locations with matching patterns. We also demonstrate that
clustering of data in the learned space correlates with biologically-meaningful
distinctions. Finally, we introduce a visualization tool and software ecosystem
to facilitate user-friendly interactive analysis and uncover interesting
biological patterns. In short, our work opens possible new avenues in
understanding of and discovery in large data sets, arising in domains such as
EM analysis.
