How can we design CNN architectures that are inherently more interpretable without sacrificing accuracy?

Aug 15, 2022 ... What neural network is appropriate for your predictive modeling problem? It can be difficult for a beginner to the field of deep learning to ... Apr 3, 2023 ... A CNN works by transforming an input image into a feature map, which is then processed through multiple convolutional and pooling layers to ... CNN is one of the popular deep learning algorithms that recently gained popularity in the sector of hydrology and earth sciences (Panahi et al., 2020). The CNN ... A Convolutional Neural Network (CNN) is a type of deep learning model designed primarily for processing grid-like data, Mar 31, 2021 ... This review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. Aug 5, 2022 ... An integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and ... Feb 8, 2022 ... ... Machine Learning, Stockholm, Sweden, PMLR 80 1–19 (2018) ... Random CNN structure: tool to increase generalization ability in deep learning. We have proposed a deep learning method called BCM-SegNet, an optimized Mask R-CNN-based algorithm for segmenting BSE images of bronze casting moulds and cores. The basic CNN architecture consists of five main layers: input, convolutional, activation, pooling, and fully connected. Each layer plays a specific role in ... Feb 23, 2023 ... The CNN and recurrent including LSTM and GRU are all supported on GPU using the native Deep Learning package.