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
Machine Learning Approach to Brain Tumor Detection and Classification
Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee, Justin Park, Kate Hwang, Sanghyeon Kim, Soo Min Oh
TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant
Guopeng Li, Qiang Wang, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia
Stress Assessment with Convolutional Neural Network Using PPG Signals
Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari
Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
Xiannan Huang, Chao Yang, Quan Yuan
Enhancing Apple's Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging
Omar Coello, Moisés Coronel, Darío Carpio, Boris Vintimilla, Luis Chuquimarca
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis
Weronika Ormaniec, Felix Dangel, Sidak Pal Singh
LKASeg:Remote-Sensing Image Semantic Segmentation with Large Kernel Attention and Full-Scale Skip Connections
Xuezhi Xiang, Yibo Ning, Lei Zhang, Denis Ombati, Himaloy Himu, Xiantong Zhen
QIANets: Quantum-Integrated Adaptive Networks for Reduced Latency and Improved Inference Times in CNN Models
Zhumazhan Balapanov, Edward Magongo, Vanessa Matvei, Olivia Holmberg, Jonathan Pei, Kevin Zhu
MOZART: Ensembling Approach for COVID-19 Detection using Chest X-Ray Imagery
Mohammed Shabo, Nazar Siddig
Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection
Chu-Hsuan Abraham Lin, Chen-Yu Liu, Samuel Yen-Chi Chen, Kuan-Cheng Chen
Learning Algorithms Made Simple
Noorbakhsh Amiri Golilarz, Elias Hossain, Abdoljalil Addeh, Keyan Alexander Rahimi
Efficient Hyperparameter Importance Assessment for CNNs
Ruinan Wang, Ian Nabney, Mohammad Golbabaee
GPR Full-Waveform Inversion through Adaptive Filtering of Model Parameters and Gradients Using CNN
Peng Jiang, Kun Wang, Jiaxing Wang, Zeliang Feng, Shengjie Qiao, Runhuai Deng, Fengkai Zhang
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization
Guangrui Yang, Ming Li, Han Feng, Xiaosheng Zhuang