Comparison of Discriminative and Generative Image Classifiers

Abstract: In this report a discriminative and a generative image classifier, used for classification of images with handwritten digits from zero to nine, are compared. The aim of this project was to compare the accuracy of the two classifiers in absence and presence of perturbations to the images. This report describes the architectures and training of the classifiers using PyTorch. Images were perturbed in four ways for the comparison. The first perturbation was a model-specific attack that perturbed images to maximize likelihood of misclassification. The other three image perturbations changed pixels in a stochastic fashion. Furthermore, The influence of training using perturbed images on the robustness of the classifier, against image perturbations, was studied. The conclusions drawn in this report was that the accuracy of the two classifiers on unperturbed images was similar and the generative classifier was more robust against the model-specific attack. Also, the discriminative classifier was more robust against the stochastic noise and was significantly more robust against image perturbations when trained on perturbed images.