What are some approaches to improve the interpretability of feature maps?

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. Feature Map Transfer: Vertical Federated Learning for CNN Models. Tianchi Sha, Xiao Yu, Zhiwei Shi, Yuan Xue, Shouxin Wang, Sikang Hu*. Feb 1, 2023 ... How to visualize CNN feature maps? I have been working on CNN but cant figure how to visualize feature maps between layers. The feature maps generated will be sent to the next layer in the deep CNN. The data volume is much higher since the input is now an image instead of a ... We will explain what feature and feature map are later on. ... This creates the spatial invariance of ConvNet. Figure 7. Visualizing CNN feature maps and ... Conv layer applies 2-D convolution with trained filters on input feature maps to extract local features. Multiple Conv layers are usually cascaded to extract ... Nov 21, 2020 ... Steps to generate feature maps:- · for layer_name, feature_map in zip(layer_names, feature_maps): · if len(feature_map.shape) == 4 k = ... CNN Feature Maps and Object Saliency Maps. Wataru Shimoda and Keiji Yanai ... map can be obtained by up-sampling saliency maps of the intermediate ... Sep 17, 2018 ... These so-called feature maps are the outputs of the convolutions that take place using the input color channels and the convolutional filters. 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 ...