|University of Toronto|
Image recognition, also known as computer vision, is one of the most prominent applications of neural networks. The image recognition methods presented in this thesis are based on the reverse process: generating images. Generating images is easier than recognizing them, for the computer systems that we have today. This work leverages the ability to generate images, for the purpose of recognizing other images.
One part of this thesis introduces a thorough implementation of this “analysis by synthesis” idea in a sophisticated autoencoder. Half of the image generation system (namely the structure of the system) is hard-coded; the other half (the content inside that structure) is learned. At the same time as this image generation system is being learned, an accompanying image recognition system is learning to extract descriptions from images. Learning together, these two components develop an excellent understanding of the provided data.
The second part of the thesis is an algorithm for training undirected generative models, by making use of a powerful interaction between training and a Markov Chain whose task is to produce samples from the model. This algorithm is shown to work well on image data, but is equally applicable to undirected generative models of other types of data.