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Interpretability of deep convolutional neural networks in image analysis

University of British Columbia

Deep Convolutional Neural Networks (DCNNs) have achieved superior performance in many computer vision tasks. However, it has been challenging to understand their internal mechanisms and explain how they make their predictions. The inability to understand how DCNNs reach their decisions could result in serious problems in real-life applications. The objective of this thesis is to design a framework that interprets the internal mechanisms of DCNNs, explains how they make decisions, and builds trust in their predictions. To recognize an object in an image, humans consider both the entirety and the local details of the object. We developed a method that shows that the deep layer of the network learns about the object as a whole, and the shallow layer learns the object’s fine-features. Thus, DCNNs reach their decisions in a way similar to humans. DCNNs are formed of layers. Each layer contains dozens or hundreds of channels that represent image features and are propagated to the subsequent layer. To understand the process of how a network makes decisions, we developed two methods. The first determines which channels are important for deciding the class of an object in an image. This method disables different channels in a layer to determine the influence of each channel on the network’s class-discriminative capability. While this method gives insight into the role of different channels (in a layer) in the classification prediction, the second method explains the role of every layer in this decision. It finds out the important information every layer learns about a certain class of objects. It starts at the deepest layer and finds the most important feature-maps that affects the DCNN final decision. It then decomposes this information into feature-maps belonging to its adjacent shallower layer, using sparse representation. The feature-maps with non-zero coefficients in this decomposition form the significant information in the shallower layer. This process is repeated for all layers in the network. Experiments have shown that the significant feature-maps of all layers result in interpretable patterns that offer a better explanation about the network learning (of intra-class images and inter-class images) than the complicated data in the network.

Text

2020-06-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74784

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/