What are the limitations of gradient-based visualization techniques for interpreting feature maps?

Jul 16, 2017 ... A feature map, or activation map, is the output activations for a given filter (a1 in your case) and the definition is the same regardless ... Apr 23, 2015 ... ... CNN) use simple multi-layer perceptrons. This paper demonstrates that carefully designing deep networks for object classification is just as ... Feature maps are a core component of convolutional neural networks as they capture essential features from the input image to aid the network in decision-making ... Jan 31, 2019 ... ... feature map sizes for CNN configuration. Six configurations are ... Classification, Functional MRI, Deep learning, CNN, Feature map. Oct 22, 2017 ... Specifically, we develop a convolutional neural network (CNN) to classify the feature map sets extracted from the tertiary protein structures. Feb 12, 2021 ... We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Nov 14, 2022 ... The network has three convolutional layers, as follows: Layer 1: Number of feature maps =8 Size of a feature map =220×220 Layer 2: Number of ... Apr 2, 2025 ... Pooling layer is used in CNNs to reduce the spatial dimensions (width and height) of the input feature maps while retaining the most important information. CNN Feature Maps and Object Saliency Maps. Wataru Shimoda and Keiji Yanai ... map can be obtained by up-sampling saliency maps of the intermediate ... In this paper, we propose strategies to enable CNN inference accelerators to have an adjustable feature map compression ratio, making them tunable regarding ...