  We present DECORAS, a deep learning based approach to detect both point and
extended sources from Very Long Baseline Interferometry (VLBI) observations.
Our approach is based on an encoder-decoder neural network architecture that
uses a low number of convolutional layers to provide a scalable solution for
source detection. In addition, DECORAS performs source characterization in
terms of the position, effective radius and peak brightness of the detected
sources. We have trained and tested the network with images that are based on
realistic Very Long Baseline Array (VLBA) observations at 20 cm. Also, these
images have not gone through any prior de-convolution step and are directly
related to the visibility data via a Fourier transform. We find that the source
catalog generated by DECORAS has a better overall completeness and purity, when
compared to a traditional source detection algorithm. DECORAS is complete at
the 7.5$\sigma$ level, and has an almost factor of two improvement in
reliability at 5.5$\sigma$. We find that DECORAS can recover the position of
the detected sources to within 0.61 $\pm$ 0.69 mas, and the effective radius
and peak surface brightness are recovered to within 20 per cent for 98 and 94
per cent of the sources, respectively. Overall, we find that DECORAS provides a
reliable source detection and characterization solution for future wide-field
VLBI surveys.
