Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
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Cites background or methods from "Ablation-CAM: Visual Explanations f..."
...Besides, they also break the axiom of implementation invariance since they are layer sensitive [4]....
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..., Grad-CAM [23], Grad-CAM++ [3] and Ablation-CAM [4])....
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...[4] proposed Ablation-CAM to remove the dependence on gradients but this method is quite time-consuming since it has to run forward propagation for hundreds of times per image....
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...Note that the original weight of each feature map in Ablation-CAM [4] is defined as Sc(F )−Sc(F\F) ||Flk|| ....
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...This definition is inspired by CAM [32] and further improved by other works, such as Grad-CAM++ [3] and Ablation-CAM [4]....
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Cites background from "Ablation-CAM: Visual Explanations f..."
...They can be divided into two branches, one is gradient-based CAMs [2], [15], which represent the linear weights corresponding to internal activation maps by gradient information....
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...As the output layer is a non-linear function, gradient-based CAMs tend to diminish the backpropagating gradients which cause gradient saturation thereby making it difficult to provide concrete explanations....
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...These categories are known as Class Activation Maps (CAMs)....
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...The other is gradient-free CAMs [4], [23] which capture the importance of each activation map by the target score in forward propagation....
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...The generalisation of CAMs take place with Grad-CAM [15]....
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References
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"Ablation-CAM: Visual Explanations f..." refers background in this paper
...Convolutional Neural Networks (CNNs) are known to show near human-level performance on various computer vision tasks such as image classification [8], object detection [5], semantic segmentation [10] and have performed well on tasks such as image captioning [19] and visual question answering [2]....
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