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논문 제목 : Informative Class Activation Maps 논문 주소 : https://arxiv.org/abs/2106.10472 Informative Class Activation Maps We study how to evaluate the quantitative information content of a region within an image for a particular label. To this end, we bridge class activation maps with information theory. We develop an informative class activation map (infoCAM). Given a classi arxiv.org 주요 내용 정리: 1) 저..
논문 제목 : Eigen-CAM: Class Activation Map Using Principal Components 논문 주소 : https://arxiv.org/abs/2008.00299 Eigen-CAM: Class Activation Map using Principal Components Deep neural networks are ubiquitous due to the ease of developing models and their influence on other domains. At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or fe..
논문 제목 : Combinational Class Activation Maps for Weakly Supervised Object Localization 논문 주소 : https://openaccess.thecvf.com/content_WACV_2020/html/Yang_Combinational_Class_Activation_Maps_for_Weakly_Supervised_Object_Localization_WACV_2020_paper.html WACV 2020 Open Access Repository Seunghan Yang, Yoonhyung Kim, Youngeun Kim, Changick Kim; Proceedings of the IEEE/CVF Winter Conference on Applica..
논문 제목 : How to Manipulate CNNs to Make Them Lie: the GradCAM Case 논문 주소 : https://arxiv.org/abs/1907.10901 How to Manipulate CNNs to Make Them Lie: the GradCAM Case Recently many methods have been introduced to explain CNN decisions. However, it has been shown that some methods can be sensitive to manipulation of the input. We continue this line of work and investigate the explanation method Gra..
논문 제목 : Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs 논문 주소 : https://arxiv.org/abs/2008.02312 Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, sev..