Occlusion method to obtain saliency maps for CNN
Abstract: This Bachelor project will study convolutional neural networks created for image classification. Furthermore, it will specifically use an explanatory model for how the network decided a certain classification output. This is to increase the interpretability of the network. However, the completeness of the explanatory model needs to be high for it to be useful. A saliency map of how valuable each image pixel is for the classification will be created, by occluding parts of the image. The MNIST dataset was used, which contains handwritten digits. The main points of research were to study ways to occlude or filter parts of the image. Among the researched topics were the size of the filter, the number of filters and how the filtered pixels should be alternated. The occlusion method to obtain saliency maps was compared with rivalling methods, such as deepLIFT. The conclusion was that the large amount of computational power needed limits the use of occlusion based methods, but the high completeness makes it useful for niche purposes.
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