  Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe
light-curves of billons or more astronomical sources. This presents new
challenges for identifying interesting and important types of variability.
Collecting a sufficient number of labelled data for training is difficult,
however, especially in the early stages of a new survey. Here we develop a
single-band light-curve classifier based on deep neural networks, and use
transfer learning to address the training data paucity problem by conveying
knowledge from one dataset to another. First we train a neural network on 16
variability features extracted from the light-curves of OGLE and EROS-2
variables. We then optimize this model using a small set (e.g. 5%) of periodic
variable light-curves from the ASAS dataset in order to transfer knowledge
inferred from OGLE/EROS-2 to a new ASAS classifier. With this we achieve good
classification results on ASAS, thereby showing that knowledge can be
successfully transferred between datasets. We demonstrate similar transfer
learning using Hipparcos and ASAS-SN data. We therefore find that it is not
necessary to train a neural network from scratch for every new survey, but
rather that transfer learning can be used even when only a small set of
labelled data is available in the new survey.
