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.
Papers
Adaptive high-precision sound source localization at low frequencies based on convolutional neural network
Wenbo Ma, Yan Lu, Yijun Liu
Learning Partial Differential Equations with Deep Parallel Neural Operator
Qinglong Ma, Peizhi Zhao, Sen Wang, Tao Song
A Self-attention Residual Convolutional Neural Network for Health Condition Classification of Cow Teat Images
Minghao Wang
CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability
Xi Zhang, Yaru Xue, Shaocheng Jia, Xin Pei
SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding
Ziyang Zhang, Andrew Thwaites, Alexandra Woolgar, Brian Moore, Chao Zhang
Spectral Wavelet Dropout: Regularization in the Wavelet Domain
Rinor Cakaj, Jens Mehnert, Bin Yang
Early diagnosis of Alzheimer's disease from MRI images with deep learning model
Sajjad Aghasi Javid, Mahmood Mohassel Feghhi
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection
Ayush Kumar Sharma, Sourav Patel, Supriya Bharat Wakchaure, Abirami S
Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation
Md. Farhadul Islam, Sarah Zabeen, Meem Arafat Manab, Mohammad Rakibul Hasan Mahin, Joyanta Jyoti Mondal, Md. Tanzim Reza, Md Zahidul Hasan, Munima Haque, Farig Sadeque, Jannatun Noor
Ophthalmic Biomarker Detection with Parallel Prediction of Transformer and Convolutional Architecture
Md. Touhidul Islam, Md. Abtahi Majeed Chowdhury, Mahmudul Hasan, Asif Quadir, Lutfa Aktar
Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes
Seraj Ghasemi, Hamed Hosseini, MohammadHossein Koosheshi, Mehdi Tale Masouleh, Ahmad Kalhor
EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation
Ao Chang, Jiajun Zeng, Ruobing Huang, Dong Ni
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution
Zhenyu Hu, Wanjie Sun
The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach
David Bertoin, Eduardo Hugo Sanchez, Mehdi Zouitine, Emmanuel Rachelson
CNN Mixture-of-Depths
Rinor Cakaj, Jens Mehnert, Bin Yang
Linking in Style: Understanding learned features in deep learning models
Maren H. Wehrheim, Pamela Osuna-Vargas, Matthias Kaschube