  Magnetic flux generated within the solar interior emerges to the surface,
forming active regions (ARs) and sunspots. Flux emergence may trigger explosive
events, such as flares and coronal mass ejections and therefore understanding
emergence is useful for space-weather forecasting. Evidence of any
pre-emergence signatures will also shed light on sub-surface processes
responsible for emergence. In this paper, we present a first analysis of
emerging ARs from the Solar Dynamics Observatory/Helioseismic Emerging Active
Regions (SDO/HEAR) dataset (Schunker et al. 2016) using deep convolutional
neural networks (CNN) to characterize pre-emergence surface magnetic-field
properties. The trained CNN classifies between pre-emergence (PE) line-of-sight
magnetograms and a control set of non-emergence (NE) magnetograms with a True
Skill Statistic (TSS) score of ~85%, 3h prior to emergence and ~40\%, 24h prior
to emergence. Our results are better than a baseline classification TSS
obtained using discriminant analysis of only the unsigned magnetic flux. We
develop a network pruning algorithm to interpret the trained CNN and show that
the CNN incorporates filters that respond positively as well as negatively to
the unsigned magnetic flux of the magnetograms. Using synthetic magnetograms,
we demonstrate that the CNN output is sensitive to the length-scale of the
magnetic regions with small-scale and intense fields producing maximum CNN
output and possibly a characteristic pre-emergence pattern. Given increasing
popularity of deep learning, techniques developed here for interpretation of
the trained CNN -- using network pruning and synthetic data -- are relevant for
future applications in solar and astrophysical data analysis.
