How can we build more transparent and accountable AI systems based on a better understanding of their internal representations, such as feature maps?

Jan 26, 2023 ... Let's start visualizing the feature maps in Convolutional Neural Networks (CNN). We will see what the Convolutional filters are looking for ... This research attempts to provide five insights of the 'Black box' mechanism of CNNs, using skin cancer dermoscopy and lung scan computed tomography (CT) Scan ... Jul 13, 2021 ... ... feature map. However, in CNN, pooling is applied to feature maps that result from each filter. Flattening. You can think of CNN as a sequence ... Aug 8, 2023 ... Pooling simplifies the feature maps in a CNN by reducing their spatial dimensions and introducing translation invariance. Max pooling, in ... Feature Map Transfer: Vertical Federated Learning for CNN Models. Tianchi Sha, Xiao Yu, Zhiwei Shi, Yuan Xue, Shouxin Wang, Sikang Hu*. Oct 22, 2017 ... Specifically, we develop a convolutional neural network (CNN) to classify the feature map sets extracted from the tertiary protein structures. Feb 11, 2021 ... We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the ... Feature maps are fundamental outputs generated by the layers within a Convolutional Neural Network (CNN), particularly the convolutional layers. A backbone is a CNN that accepts an input image and produces a feature map or set of feature maps having higher semantic content. Accurate detection ...