Dataset Pre-Processing and Artificial Augmentation, Network Architecture and Training Parameters used in Appropriate Training of Convolutional Neural Networks for Classification Based Computer Vision Applications: A Survey

Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications

A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further