Using Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks for Partially Occluded Object Recognition

Using Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks for Partially Occluded Object Recognition Joseph Lin Chu Artificial neural networks have been widely used for machine learning tasks such as object recognition. Recent developments have made use of biologically inspired architectures, such as the Convolutional Neural Network, and the Deep Belief Network. A theoretical method for estimating the optimal number of feature maps for a Convolutional Neural Network maps using the dimensions of the receptive field or convolutional kernel is proposed. Empirical experiments are performed that show that the method works to an extent for extremely small receptive fields, but doesn’t generalize as clearly to all receptive field sizes. We then test the hypothesis that generative models such as the Deep Belief Network should perform better on occluded object recognition tasks than purely discriminative models such as Convolutional Neural Networks. We find that the data does not support this hypothesis when the generative models are run in a partially discriminative manner. We also find that the use of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to classify non-occluded images.