What are the applications of feature maps in other domains like time series analysis, graph analysis, or bioinformatics where CNN-like architectures are being explored?

In this paper, we design a light CNN architecture that addresses these challenges. Specifically, we implemented Max-feature-map (MFM) into each convolutional ... Jan 22, 2025 ... ... features, and ultimately infer what object they constitute. To start, the CNN receives an input feature map: a three-dimensional matrix ... 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 ... (CNN + LSTM) (CNN + Reinforcement Learning). Page 4. Convolutional ... • Line Buffer Size << Feature Map Size. Feature Map. 3*3 Kernel x. Layer i. ✔️. neural network or say (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine ... In the top-down pathway, a subsequent feature map ... Jan 31, 2019 ... ... feature map sizes for CNN configuration. Six configurations are ... Classification, Functional MRI, Deep learning, CNN, Feature map. Mar 26, 2018 ... Just like Mask R-CNN, FPN is also good at extracting masks for image segmentation. Using MLP, a 5 × 5 window is slide over the feature maps to ... Jun 22, 2018 ... A convolution layer is a fundamental component of the CNN architecture that performs feature ... feature map, whereas the depth of feature maps is ... 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 ... Proposed method converts ...