Fig. 4
From: Revealing ferroelectric switching character using deep recurrent neural networks
Features learned from low-dimensional layer of the resonance response autoencoder. a, e, i Feature maps extracted from low-dimensional layer of autoencoder trained on the resonance 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, f, j Average activation across the domain bands superimposed onto the average topography. Neural network generated c, g, k resonance hysteresis loops and d, h, l piezoresponse hysteresis loops. In all figures the color of the curves/images reflect the normalized activation from the low-dimensional layer at that location or from the generated response curve. Numbers in figures represent observations of ferroelectric or ferroelastic switching events
