Understanding Convolutional Neural Networks

This seminar paper focusses on convolutional neural networks and a visualization technique allowing further insights into their internal operation. After giving a brief introduction to neural networks and the multilayer perceptron, we review both supervised and unsupervised training of neural networks in detail. In addition, we discuss several approaches to regularization. The second section introduces the different types of layers present in recent convolutional neural networks. Based on these basic building blocks, we discuss the architecture of the traditional convolutional neural network as proposed by LeCun et al. [LBD+89] as well as the architecture of recent implementations. The third section focusses on a technique to visualize feature activations of higher layers by backprojecting them to the image plane. This allows to get deeper insights into the internal working of convolutional neural networks such that recent architectures can be evaluated and improved even further.