  The scale of ongoing and future electromagnetic surveys pose formidable
challenges to classify astronomical objects. Pioneering efforts on this front
include citizen science campaigns adopted by the Sloan Digital Sky Survey
(SDSS). SDSS datasets have been recently used to train neural network models to
classify galaxies in the Dark Energy Survey (DES) that overlap the footprint of
both surveys. Herein, we demonstrate that knowledge from deep learning
algorithms, pre-trained with real-object images, can be transferred to classify
galaxies that overlap both SDSS and DES surveys, achieving state-of-the-art
accuracy $\gtrsim99.6\%$. We demonstrate that this process can be completed
within just eight minutes using distributed training. While this represents a
significant step towards the classification of DES galaxies that overlap
previous surveys, we need to initiate the characterization of unlabelled DES
galaxies in new regions of parameter space. To accelerate this program, we use
our neural network classifier to label over ten thousand unlabelled DES
galaxies, which do not overlap previous surveys. Furthermore, we use our neural
network model as a feature extractor for unsupervised clustering and find that
unlabeled DES images can be grouped together in two distinct galaxy classes
based on their morphology, which provides a heuristic check that the learning
is successfully transferred to the classification of unlabelled DES images. We
conclude by showing that these newly labeled datasets can be combined with
unsupervised recursive training to create large-scale DES galaxy catalogs in
preparation for the Large Synoptic Survey Telescope era.
