  The transfer learning of a neural network is one of its most outstanding
aspects and has given supervised learning with neural networks a prominent
place in data science. Here we explore this feature in the context of strongly
interacting many-body systems. Through case studies, we test the potential of
this deep learning technique to detect phases and their transitions in
frustrated spin systems, using fully-connected and convolutional neural
networks. In addition, we explore a recently-introduced technique, which is at
the middle point of supervised and unsupervised learning. It consists in
evaluating the performance of a neural network that has been deliberately
"confused" during its training. To properly demonstrate the capability of the
"confusion" and transfer learning techniques, we apply them to a paradigmatic
model of frustrated magnetism in two dimensions, to determine its phase diagram
and compare it with high-performance Monte Carlo simulations.
