Comments on the minimal training set for CNN: a case study of the frustrated $J_1$-$J_2$ Ising model on the square lattice

Abstract: The minimal training set to train a working CNN is explored in detail. The considered model is the frustrated $J_1$-$J_2$ Ising model on the square lattice. Here $J_1 < 0$ and $J_2 > 0$ are the nearest and next-to-nearest neighboring couplings, respectively. We train the CNN using the configurations of $g \stackrel{\text{def}}{=} J_2/|J_1| = 0.7$ and employ the resulting CNN to study the phase transition of $g = 0.8$. We find that this transfer learning is successful. In particular, only configurations of two temperatures, one is below and one is above the critical temperature $T_c$ of $g=0.7$, are needed to reach accurately determination of the $T_c$ of $g=0.8$. However, it may be subtle to use this strategy for the training. Specifically, for the considered model, due to the inefficiency of the single spin flip algorithm used in sampling the configurations at the low-temperature region, the two temperatures associated with the training set should not be too far away from the $T_c$ of $g=0.7$, otherwise, the performance of the obtained CNN is not of high quality, hence cannot determine the $T_c$ of $g=0.8$ accurately. For the considered model, we also uncover the condition for training a successful CNN when only configurations of two temperatures are considered as the training set.