  Citizen science is gaining popularity as a valuable tool for labelling large
collections of astronomical images by the general public. This is often
achieved at the cost of poorer quality classifications made by amateur
participants, which are usually verified by employing smaller data sets
labelled by professional astronomers. Despite its success, citizen science
alone will not be able to handle the classification of current and upcoming
surveys. To alleviate this issue, citizen science projects have been coupled
with machine learning techniques in pursuit of a more robust automated
classification. However, existing approaches have neglected the fact that,
apart from the data labelled by amateurs, (limited) expert knowledge of the
problem is also available along with vast amounts of unlabelled data that have
not yet been exploited within a unified learning framework. This paper presents
an innovative learning methodology for citizen science capable of taking
advantage of expert- and amateur-labelled data, featuring a transfer of labels
between experts and amateurs. The proposed approach first learns from
unlabelled data with a convolutional autoencoder and then exploits amateur and
expert labels via the pre-training and fine-tuning of a convolutional neural
network, respectively. We focus on the classification of galaxy images from the
Galaxy Zoo project, from which we test binary, multi-class, and imbalanced
classification scenarios. The results demonstrate that our solution is able to
improve classification performance compared to a set of baseline approaches,
deploying a promising methodology for learning from different confidence levels
in data labelling.
