By analyzing the feature maps of misclassified examples, can we understand why the model failed?

“Unit” refers either to individual neurons, channels (also called feature maps) ... It links highly activated areas of CNN channels with human concepts (objects, ... Implementation details. The conventional CNN has three convolutional layers with 96, 128 and 256 filters, re- spectively. Each layer consists of a 5 × ... The implemented techniques are: feature map visualization, backward optimization and layer-wise relevance propagation. Since our model reaches a good accuracy ... 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 ... This article presents a hybrid quantum-classical framework by incorporating quantum feature maps regulated classical Convolutional Neural Network (CNN) ... The two-dimensional filters learned by the model can be inspected and visualized to discover the types of features that the model will detect. For every proposed plane, the feature maps of the reference image are stacked on top of the war- ped feature map of the other image. This produces a cost ... Jun 29, 2021 ... This paper proposes an emotion recognition method based on three-dimensional (3D) feature maps and CNNs. 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 = ... Mainly feature maps from pretrained Convolutional Nueral Networks(CNN) is going to be utilized as a non-linear feature transform. Those feature maps ...