  A transfer learning method, Domain Adversarial Neural Network (DANN), is
introduced to study the phase transition of two-dimensional q-state Potts
model. With the DANN, we only need to choose a few labeled configurations
automatically as input data, then the critical points can be obtained after
training the algorithm. By an additional iterative process, the critical points
can be captured to comparable accuracy to Monte Carlo simulations as we
demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first
or second-order) is also determined at the same time. Meanwhile, for the
second-order phase transition at q=3, we can calculate the critical exponent
$\nu$ by data collapse. Furthermore, compared to the traditional supervised
learning, we found the DANN to be more accurate with lower cost.
