  We investigate the use of deep convolutional neural networks (deep CNNs) for
automatic visual detection of galaxy mergers. Moreover, we investigate the use
of transfer learning in conjunction with CNNs, by retraining networks first
trained on pictures of everyday objects. We test the hypothesis that transfer
learning is useful for improving classification performance for small training
sets. This would make transfer learning useful for finding rare objects in
astronomical imaging datasets. We find that these deep learning methods perform
significantly better than current state-of-the-art merger detection methods
based on nonparametric systems like CAS and GM$_{20}$. Our method is end-to-end
and robust to image noise and distortions; it can be applied directly without
image preprocessing. We also find that transfer learning can act as a
regulariser in some cases, leading to better overall classification accuracy
($p = 0.02$). Transfer learning on our full training set leads to a lowered
error rate from 0.038 $\pm$ 1 down to 0.032 $\pm$ 1, a relative improvement of
15%. Finally, we perform a basic sanity-check by creating a merger sample with
our method, and comparing with an already existing, manually created merger
catalogue in terms of colour-mass distribution and stellar mass function.
