Can we use feature maps to estimate the uncertainty of a CNN's prediction?

Feb 22, 2019 ... Specifically, the task involves identifying useful 2D-CNN input feature maps for enhancing speaker identification with an ultimate goal to ... In this next section, you're going to take a peek inside your network to see what sorts of features your models are learning at different layers. The two-dimensional filters learned by the model can be inspected and visualized to discover the types of features that the model will detect. 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 ... A novel hybrid FME-Residual-HSCMT technique is introduced, comprised of residual CNN and Transformer concepts to capture global and local fine-grained AD ... Jun 30, 2023 ... Have you ever used deep learning to solve computer vision tasks? If so, you probably trained a convolutional neural network (ConvNet or CNN) ... Oct 28, 2022 ... map of layer k, ak is the activation map of layer k. In this study, we visualize the feature maps fk. 258. 2.5.2 Backward optimisation (BO). Oct 14, 2021 ... ... CNN model using the time–frequency feature maps to classify the eight types of arrhythmias and normal sinus rhythm. The standard 12-lead ECG ... This article presents a hybrid quantum-classical framework by incorporating quantum feature maps regulated classical Convolutional Neural Network (CNN) ... May 17, 2015 ... In a Convolutional Neural Network (CNN), a feature map is the output of one filter applied to the previous layer. The concept of a feature map ...