  We investigate the performance of neural networks in identifying critical
behaviour in the 2D Ising model with next-to-nearest neighbour interactions. We
train DNN and CNN based classifiers on the Ising model configurations with
nearest neighbour interactions and test the ability of our models to identify
the critical (cross-over) region, as well as the phases in the presence of
next-to-nearest neighbour interactions. Our main objective is to investigate
whether our models can learn the notion of criticality and universality, in
contrast to merely identifying the phases by simply learning the value of the
order parameter, magnetization in this case. We design a simple adversarial
training process which forces our models to ignore learning about the order
parameter while training. We find that such models are able to identify
critical region with reasonable success, implying that it has learned to
identify the structure of spin-spin correlations near criticality.
