What are some examples of efficient CNN architectures (e.g., MobileNets, ShuffleNets) and how do they achieve efficiency?

Jun 16, 2021 ... ... (CNN) to classify the AGILE-GRID intensity maps by improving the GRB ... feature engineering (feature extraction) from the raw data ... Nov 21, 2022 ... In Convolutional Neural Networks we extract and create abstractified “feature maps” of our given image. My thought was this: We extract things like lines ... The implemented techniques are: feature map visualization, backward optimization and layer-wise relevance propagation. Since our model reaches a good accuracy ... Jun 13, 2019 ... You create a random image. You feed it into the network and compute a loss that is actually minus the mean value of the feature map ... Our method does not require fine-tuning the network weights and halves the data transfer volumes to the main memory by compressing feature maps, which are ... ... map during 20–60 s interval of N-back tasks. Our results indicate that ... CNN model with ΔHbO maps and temporal feature maps above. Finally, we used ... Jan 29, 2017 ... Instead of using max-pooling layers to reduce the size of the feature maps, we use convolution layers with increased strides. 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 ... Implementation details. The conventional CNN has three convolutional layers with 96, 128 and 256 filters, re- spectively. Each layer consists of a 5 × ... Feb 11, 2021 ... We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images.