  Machine learning techniques exhibit significant performance in discriminating
different phases of matter and provide a new avenue for studying phase
transitions. We investigate the phase transitions of three dimensional
$q$-state Potts model on cubic lattice by using a transfer learning approach,
Domain Adversarial Neural Network (DANN). With the unique neural network
architecture, it could evaluate the high-temperature (disordered) and
low-temperature (ordered) phases, and identify the first and second order phase
transitions. Meanwhile, by training the DANN with a few labeled configurations,
the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy,
which are consistent with those of the Monte Carlo simulations. These findings
would promote us to learn and explore the properties of phase transitions in
high-dimensional systems.
