The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process. But Convolutional Neural Networks (CNN) have provided an alternative for automatically learning the domain specific features. Now every problem in the broader domain of computer vision is re-examined from the perspective of this new methodology. Therefore it is essential to figure-out the type of network specific to a problem. In this work, we have done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning. With AlexNet as the base CNN model, we have reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same. We hope this piece of article will really serve as a guide for any neophyte in the area.
N. Aloysius and M. Geetha, “A review on deep convolutional neural networks”, in 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2017.