iMTE

논문 제목 : Grad-CAM Guided Channel-spatial Attention Module for Fine-grained Visual Classification 논문 주소 : https://arxiv.org/abs/2101.09666 Grad-CAM guided channel-spatial attention module for fine-grained visual classification Fine-grained visual classification (FGVC) is becoming an important research field, due to its wide applications and the rapid development of computer vision technologies. Th..

논문 제목 : Ablation-CAM: Visual Explanations for Deep Convolutional Network Via Gradient-free Localization 논문 주소 : https://openaccess.thecvf.com/content_WACV_2020/html/Desai_Ablation-CAM_Visual_Explanations_for_Deep_Convolutional_Network_via_Gradient-free_Localization_WACV_2020_paper.html WACV 2020 Open Access Repository Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-..

논문 제목 : SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization 논문 주소 : https://arxiv.org/abs/2006.14255 SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments. To expl..

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 : 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 : Grad-CAM++: Generalized Gradient-based Visual Explanations for Deep Convolutional Networks 논문 주소 : arxiv.org/pdf/1710.11063.pdf IEEE WACV (2018, ieeexplore.ieee.org/document/8354201)에 나온 논문을 바탕으로 이해하고 내용을 작성한다. arixv에서 나온 버전이 좀 더 extended version임으로 Grad-CAM++에 더 깊은 이해를 위해서는 extended version을 읽는 것을 추천한다. 주요 내용 : 1) Deep models은 "black box"로서 internal function을 이해하는데에는 어려움이 있다. 이를 해결하기 위해..

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..