Comparison of class activation maps & gradient based class activation (GRAD-CAM) algorithm

This research project aims to investigate the heat map visualization techniques used for classifying images. The focus will be on Class Activation Mapping and Gradient Class Activation Mapping technique. The process includes implementation of the algorithms and proceed to do testing with different i...

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書目詳細資料
主要作者: Lim, Cheng Yun
其他作者: Deepu Rajan
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2023
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在線閱讀:https://hdl.handle.net/10356/165964
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機構: Nanyang Technological University
語言: English
實物特徵
總結:This research project aims to investigate the heat map visualization techniques used for classifying images. The focus will be on Class Activation Mapping and Gradient Class Activation Mapping technique. The process includes implementation of the algorithms and proceed to do testing with different images. The algorithm will be implemented using PyTorch and used on pre-trained models. The dataset used in the experiments were from the ImageNet. CAM uses global average pooling to generate a heatmap, while Grad-CAM uses gradients of the output class score with respect to the feature maps of the last convolutional layer to generate a more localized heatmap.