Deep Learning and Transfer Learning

Dear Colleagues,

Outstanding achievements have been gained by supervised learning in the last decade. With the introduction of deep learning models, it is possible to achieve great results with minimum domain knowledge. Human-level or, in some cases, better than human-level accuracy is achieved. However, most of this deep learning model-building relies on vast amounts of labeled data. In most cases, a massive quantity of leveled data is expensive; in some specific circumstances, it is difficult to collect a large set of data due to the nature of the problem. Deep Learning and Transfer Learning can solve these problems. This Special Issue of Deep Learning and Transfer Learning aims to present state-of-the-art research, on both theoretical issues and applications, based on Deep Learning and Transfer Learning. Papers should emphasize either theoretical issues or practical applications such as Multi-Layer Perceptrons, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Deep Belief Networks, etc., and their application. 

Dr. Sheikh Shanawaz Mostafa
Guest Editor

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