Convolutional Neural Network
Convolutional Neural Networks (CNNs) are a class of deep learning models designed for processing grid-like data, excelling in image analysis and related tasks. Current research focuses on improving CNN efficiency and robustness, exploring architectures like EfficientNet and Swin Transformers, as well as novel approaches such as Mamba models to address limitations in computational cost and long-range dependency capture. This active field of research has significant implications across diverse applications, including medical image analysis (e.g., cancer detection, Alzheimer's diagnosis), damage assessment, and art forgery detection, demonstrating the power of CNNs for automating complex visual tasks.
2331papers
Papers - Page 11
December 11, 2024
December 10, 2024
Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning
Post-Training Non-Uniform Quantization for Convolutional Neural Networks
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications
December 9, 2024
December 6, 2024
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December 4, 2024
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
December 3, 2024