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 16
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Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements
Leaky ReLUs That Differ in Forward and Backward Pass Facilitate Activation Maximization in Deep Neural Networks
Enhancing Generalization in Convolutional Neural Networks through Regularization with Edge and Line Features
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification
October 21, 2024
Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation
On the VC dimension of deep group convolutional neural networks
Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws
Disambiguating Monocular Reconstruction of 3D Clothed Human with Spatial-Temporal Transformer
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications