Editorial: In Memoriam: Anastasios (Tas) N. Venetsanopoulos

[No abstract available

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

A New Fuzzy Additive Noise Reduction Method

In this paper we present a new alternative noise reduction method for color images that were corrupted with additive Gaussian noise. We illustrate that color images have to be processed in a different way than most of the state-of-the-art methods. The proposed method consists of two sub-filters. The main concern of the first subfilter is to distinguish between local variations due to noise and local variations due to image structures such as edges. This is realized by using the color component distances instead of component differences as done by most current filters. The second subfilter is used as a complementary filter which especially preserves differences between the color components. This is realized by calculating the local differences in the red, green and blue environment separately. These differences are then combined to calculate the local estimation of the central pixel. Experimental results show the feasibility of the proposed approach