Fig. 5: Learning by confusion.
From: Machine learning of phase transitions in nonlinear polariton lattices
a An example W-shape of the accuracy of neural network training during learning by confusion. We fixed the tunnelling rate J and varied the lattice gain parameter W, observing peak accuracy in training when the hypothetical labelling coincided with the genuine one. The insets show cartoons for possible types of labelling. Circles show genuine labelling corresponding to two phases (yellow and blue), with the true critical point placed in the middle. The hypothetical labelling is shown by stars. b The structure of the neural network used in learning by confusion. It contains three layers: input layer (64 × 3 neurons), hidden dense layer (80 neurons) and output dense layer (2 outputs).
