  We present BlendHunter, a proof-of-concept for a deep transfer learning based
approach for the automated and robust identification of blended sources in
galaxy survey data. We take the VGG-16 network with pre-trained convolutional
layers and train the fully connected layers on parametric models of COSMOS
images. We test the efficacy of the transfer learning by taking the weights
learned on the parametric models and using them to identify blends in more
realistic CFIS-like images. We compare the performance of this method to SEP (a
Python implementation of SExtractor) as function of noise level and the
separation between sources. We find that BlendHunter outperforms SEP by $\sim
15\%$ in terms of classification accuracy for close blends ($<10$ pixel
separation between sources) regardless of the noise level used for training.
Additionally, the method provides consistent results to SEP for distant blends
($\geq10$ pixel separation between sources) provided the network is trained on
data with a relatively close noise standard deviation to the target images. The
code and data have been made publicly available to ensure the reproducibility
of the results.
