  Vetting of exoplanet candidates in transit surveys is a manual process, which
suffers from a large number of false positives and a lack of consistency.
Previous work has shown that Convolutional Neural Networks (CNN) provide an
efficient solution to these problems. Here, we apply a CNN to classify planet
candidates from the Next Generation Transit Survey (NGTS). For training
datasets we compare both real data with injected planetary transits and
fully-simulated data, as well as how their different compositions affect
network performance. We show that fewer hand labelled lightcurves can be
utilised, while still achieving competitive results. With our best model, we
achieve an AUC (area under the curve) score of $(95.6\pm{0.2})\%$ and an
accuracy of $(88.5\pm{0.3})\%$ on our unseen test data, as well as
$(76.5\pm{0.4})\%$ and $(74.6\pm{1.1})\%$ in comparison to our existing manual
classifications. The neural network recovers 13 out of 14 confirmed planets
observed by NGTS, with high probability. We use simulated data to show that the
overall network performance is resilient to mislabelling of the training
dataset, a problem that might arise due to unidentified, low signal-to-noise
transits. Using a CNN, the time required for vetting can be reduced by half,
while still recovering the vast majority of manually flagged candidates. In
addition, we identify many new candidates with high probabilities which were
not flagged by human vetters.
