Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications

Within the world of machine learning there exists a wide range of different methods with respective advantages and applications. This paper seeks to present and discuss one such method, namely Convolutional Neural Networks (CNNs). CNNs are deep neural networks that use a special linear operation called convolution. This operation represents a key and distinctive element of CNNs, and will therefore be the focus of this method paper. The discussion starts with the theoretical foundations that underlie convolutions and CNNs. Then, the discussion proceeds to discuss some improvements and augmentations that can be made to adapt the method to estimate a wider set of function classes. The paper mainly investigates two ways of improving the method: by using locally connected layers, which can make the network less invariant to translation, and tiled convolution, which allows for the learning of more complex invariances than standard convolution. Furthermore, the use of the Fast Fourier Transform can improve the computational efficiency of convolution. Subsequently, this paper discusses two applications of convolution that have proven to be very effective in practice. First, the YOLO architecture is a state of the art neural network for image object classification, which accurately predicts bounding boxes around objects in images. Second, tumor detection in mammography may be performed using CNNs, accomplishing 7.2% higher specificity than actual doctors with only .3% less sensitivity. Finally, the invention of technology that outperforms humans in different fields also raises certain ethical and regulatory questions that are briefly discussed.

Application of Facial Recognition using Convolutional Neural Networks for Entry Access Control

The purpose of this paper is to design a solution to the problem of facial recognition by use of convolutional neural networks, with the intention of applying the solution in a camera-based home-entry access control system. More specifically, the paper focuses on solving the supervised classification problem of taking images of people as input and classifying the person in the image as one of the authors or not. Two approaches are proposed: (1) building and training a neural network called WoodNet from scratch and (2) leveraging transfer learning by utilizing a network pre-trained on the ImageNet database and adapting it to this project's data and classes. In order to train the models to recognize the authors, a dataset containing more than 150 000 images has been created, balanced over the authors and others. Image extraction from videos and image augmentation techniques were instrumental for dataset creation. The results are two models classifying the individuals in the dataset with high accuracy, achieving over 99% accuracy on held-out test data. The pre-trained model fitted significantly faster than WoodNet, and seems to generalize better. However, these results come with a few caveats. Because of the way the dataset was compiled, as well as the high accuracy, one has reason to believe the models over-fitted to the data to some degree. An added consequence of the data compilation method is that the test dataset may not be sufficiently different from the training data, limiting its ability to validate generalization of the models. However, utilizing the models in a web-cam based system, classifying faces in real-time, shows promising results and indicates that the models generalized fairly well for at least some of the classes (see the accompanying video).