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

CORRESPONDENCE ADDRESS:

2 Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA) based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size ” (SSS) problem which is often encountered in FR tasks. In this short paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed here method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the Eigenfaces, Fisherfaces and D-LDA methods

Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications

Abstract Face detection in video sequence is becoming popular in surveillance applications. The tradeoff between obtaining discriminative features to achieve accurate detection versus computational overhead of extracting these features, which affects the classification speed, is a persistent problem. This paper proposes to use multiple instances of rotational Local Binary Patterns (LBP) of pixels as features instead of using the histogram bins of the LBP of pixels. The multiple features are selected using the sequential forward selection algorithm we called Co-occurrence of LBP (CoLBP). CoLBP feature extraction is computationally efficient and produces a high-performance rate. CoLBP features are used to implement a frontal face detector applied on a 2D low-resolution surveillance sequence. Experiments show that the CoLBP face features outperform state-of-the-art Haar-like features and various other LBP features extensions. Also, the CoLBP features can tolerate a wide range of illumination and blurring changes