iMTE

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 : Score-CAM : Score-weighted visual explanations for convolutional neural networks 논문 주소 : https://openaccess.thecvf.com/content_CVPRW_2020/html/w1/Wang_Score-CAM_Score-Weighted_Visual_Explanations_for_Convolutional_Neural_Networks_CVPRW_2020_paper.html CVPR 2020 Open Access Repository Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu; Proceedi..

논문 제목 : Interpretable and fine-grained visual explanations for CNNs 논문 주소 : openaccess.thecvf.com/content_CVPR_2019/html/Wagner_Interpretable_and_Fine-Grained_Visual_Explanations_for_Convolutional_Neural_Networks_CVPR_2019_paper.html CVPR 2019 Open Access Repository Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Jorg Wagner, Jan Mathias Kohler, Tobias Gindel..

논문 제목 : Sanity checks for saliency maps 논문 주소 : arxiv.org/abs/1810.03292 Sanity Checks for Saliency Maps Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an a arxiv.org 주요 수식 정리: 0) Definition in..

논문 제목 : SmoothGrad : removing noise by adding noise 논문 주소 : arxiv.org/abs/1706.03825 SmoothGrad: removing noise by adding noise Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score arxiv.org 주요 ..

논문 제목 : Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models 논문 주소 : arxiv.org/abs/1908.01224 Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models Gaining insight into how deep convolutional neural network models perform image classification and how to explain their outpu..

논문 제목 : Learning deep features for discriminative localization 논문 주소 : openaccess.thecvf.com/content_iccv_2017/html/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.html ICCV 2017 Open Access Repository Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra; Pr..

논문 제목 : Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization 논문 주소 : openaccess.thecvf.com/content_iccv_2017/html/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.html ICCV 2017 Open Access Repository Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, De..