  A significant challenge in the tagging of boosted objects via
machine-learning technology is the prohibitive computational cost associated
with training sophisticated models. Nevertheless, the universality of QCD
suggests that a large amount of the information learnt in the training is
common to different physical signals and experimental setups. In this article,
we explore the use of transfer learning techniques to develop fast and
data-efficient jet taggers that leverage such universality. We consider the
graph neural networks LundNet and ParticleNet, and introduce two prescriptions
to transfer an existing tagger into a new signal based either on fine-tuning
all the weights of a model or alternatively on freezing a fraction of them. In
the case of $W$-boson and top-quark tagging, we find that one can obtain
reliable taggers using an order of magnitude less data with a corresponding
speed-up of the training process. Moreover, while keeping the size of the
training data set fixed, we observe a speed-up of the training by up to a
factor of three. This offers a promising avenue to facilitate the use of such
tools in collider physics experiments.
