Fig. 3
From: Revealing ferroelectric switching character using deep recurrent neural networks
Features learned from low-dimensional layer of the piezoresponse autoencoder. a, d Feature maps extracted from low-dimensional layer of autoencoder trained on piezoresponse hysteresis loops. Color indicates the magnitude of the latent feature or the activation observed in each spectra at a given pixel position. Activation is mapped in normalized units as shown in colorbar. b, e Average activation across the domain bands superimposed onto the average topography. c, f Neural network generated piezoelectric hysteresis loops as the magnitude of the activation is increased. In all figures the color of the curves/images reflect the normalized activation from the low-dimensional representation at that location or from the generated response curve
