2018/09/25 by Kyongsik Yun, Yun, Kyongsik, Alexander Huyen +3 · 35 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Image Processing Techniques #Artificial intelligence #Artificial neural network #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Deep neural networks #FOS: Computer and information sciences #Field (mathematics) #Generative Adversarial Networks and Image Synthesis #Human visual system model #Image (mathematics) #Machine Learning (cs.LG) #Machine learning #Pattern recognition (psychology) #Process (computing) #Segmentation #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1809.09645
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/09/25 · openalex publication_date 2018/09/25 · arxiv updated 2018/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.