Deep Neural Networks for Pattern Recognition

Abstract

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.

References in corpus (5)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
• Deep Image: Scaling up Image Recognition
• Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation

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