Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.Comment: 9 pages, 9 figures, Accepted at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

Visual Post-hoc Explanation of Convolutional Neural Networks Via Attribution-based Perturbation

Visual explanation algorithms are a subset of post-hoc solutions in Explainable AI that aim to open the black-box of cumbersome models utilized for image recognition tasks. These algorithms aim to determine the features in each given input data that contribute the most to the model's prediction. Producing high-resolution and class-discriminative visual explanations that accurately represent the evidence leading the model to make its predictions is a common challenge in prior visual explanation solutions. This thesis proposes a novel CNN-specific visual explanation algorithm that circumvents these limitations by extracting and aggregating semantic and spatial information from various layers of CNN. The performance, complexity, and compatibility of our proposed algorithm are analyzed through thorough comparisons with state-of-the-art methods in large-scale experiments on natural scene object recognition and visual anomaly inspection applications. Furthermore, this thesis introduces a small-scale benchmark for validating the performance of visual explanation methods on a pattern classification task.M.A.S