  In the field of pattern recognition research, the method of using deep neural
networks based on improved computing hardware recently attracted attention
because of their superior accuracy compared to conventional methods. Deep
neural networks simulate the human visual system and achieve human equivalent
accuracy in image classification, object detection, and segmentation. This
chapter introduces the basic structure of deep neural networks that simulate
human neural networks. Then we identify the operational processes and
applications of conditional generative adversarial networks, which are being
actively researched based on the bottom-up and top-down mechanisms, the most
important functions of the human visual perception process. Finally, recent
developments in training strategies for effective learning of complex deep
neural networks are addressed.
