Study of Preprocessing and Training for Deep Convolutional Generative Adversarial Networks with Atomic Resolution Transmission Electron Microscope Images.

Jongyeong Lee1 and Zonghoon Lee1

1School of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea

E-mail: zhlee@unist.ac.kr

Deep learning is a data processing method that consists of multi-layer artificial neural network structures. In particular, Generative Adversarial Networks (GANs) are unsupervised learning which remove a labeling step in a preprocessing, and extract hidden information in complex system by competing with a generative neural network and a discriminative neural network. These methods are expected to be used in analysis of Transmission Electron Microscope(TEM) images which imply complex system. Despite the beneficial characteristics of GANs for use in TEM image processing, finding good preprocessing and training frameworks remain challenge for adapting new dataset.

In this study, deep convolutional GANs were used for a parameter sharing. Various image processing methods were applied to a preprocessing step for finding optimal input dataset features. A generative network was used as a feed back factor for training step, and we used atomic resolution graphene images, obtained by an aberration-corrected FEI Titan Cubed TEM (FEI Titan3 G2 60-300).

Keywords: deep learning, generative adversarial network. preprocessing, graphene