  In this paper, a modified method of anomaly detection using convolutional
autoencoders is employed to predict phase transitions in several statistical
mechanical models on a square lattice. We show that, when the autoencoder is
trained with input data of various phases, the mean-square-error loss function
can serve as a measure of disorder, and its standard deviation becomes an
excellent indicator of critical points. We find that various types of phase
transition points, including first-order, second-order, and topological ones,
can be faithfully detected by the peaks in the standard deviation of the loss
function. Besides, the values of transition points can be accurately determined
under the analysis of finite-size scaling. Our results demonstrate that the
present approach has general application in identification/classification of
phase transitions even without a priori knowledge of the systems in question.
