Asifullah Khan

Title Recent Architectural Advancements in Deep Convolutional Neural Networks
Abstract

Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown state-of-the-art performance on various competitive benchmarks. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages (hidden layers) that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware processing units has accelerated the research in CNNs, and recently very interesting deep CNN architectures are reported. The recent race in developing deep CNNs shows that the innovative architectural ideas, as well as parameter optimization, can improve CNN performance. In this regard, different ideas in the CNN design have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and restructuring of the processing units. However, the major improvement in representational capacity of the deep CNN is achieved by the restructuring of the processing units. Especially, the idea of using a block as a structural unit instead of a layer is receiving substantial attention. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature map exploitation, channel boosting, and attention

Bio

Dr. Asifullah Khan has more than 20 years of research experience and is working as Professor in PIEAS. . He has been awarded President’s Award for Pride of Performance for year 2018. In addition, he has received four HEC's Outstanding Research Awards and one Best University Teachers Award. He has also received PAS-COMSTECH Prize 2011 in Computer Science & I.T. He has received Research Productivity Awards from Pakistan Council for Science and Technology (PCST), in year 2012, 2013, 2014, 2015, and 2106. In the field of Machine Learning and Pattern Recognition, he has 105 International Journal, 53 Conference, and 09 Book-Chapter publications to his credit. He has successfully supervised 16 PhD scholars so far and is on the Panel of Reviewers of 48 ISI International Journals. Dr. Asifullah Khan has won 7 research grants as Principal Investigator. His research interests include: Machine Learning, Deep Neural Networks, Image Processing, and Pattern Recognition. He is Head of Department of Computer and Information Sciences at PIEAS, since 2016