Advances in Deep Learning Ⅱ

Dear Colleagues,

The concept of deep learning has been applied to numerous research areas, such as prediction, classification, image/speech recognition, vision, etc., and has drastically surpassed traditional methodologies. The main difference between other methods and deep learning lies in providing a computational model for multiprocessing neural network layers by learning and representing data at multiple levels. Therefore, deep learning is easy to implicitly understand the complex structure of large data sets.

Therefore, deep learning models are able to give intuitions to understand the complex structures of large data sets. Recently, deep learning methods have actively been extended to other parts of machine learning, including reinforcement learning and transfer/meta learning, while standard deep learning methods such as RNN (recurrent neural network) and CNN (convolutional neural network) algorithms have also been extensively studied and applied to diverse industrial fields. The aim of our Special Issue is to examine the latest theoretical and practical applications of deep learning to various fields. In this connection, research related to the application of the newest deep learning techniques to image processing and data analysis can be used to improve the models derived from previous studies.

The purpose of our Special Issue is to contribute to the demonstration of new algorithms and application domains of deep learning to solve problems in various research areas. Eventually, we are to promote research and development of deep learning for multimodal data, by publishing high-quality research articles and reviews/tutorials in this rapidly growing interdisciplinary field.

Dr. Wonjoon Kim
Prof. Dr. Youngdoo Son
Prof. Dr. Jung Woo Baek
Guest Editors

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