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
MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators
Cheng Wan, Runkao Tao, Zheng Du, Yang Katie Zhao, Yingyan Celine Lin
Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks
Petr Kisselev, Padmanabhan Seshaiyer
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model
Haixu Liu, Penghao Jiang, Zerui Tao
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning
Tun-Chieh Lou, Chung-Che Wang, Jyh-Shing Roger Jang, Henian Li, Lang Lin, Norman Chang
FPGA-based Acceleration of Neural Network for Image Classification using Vitis AI
Zhengdong Li, Frederick Ziyang Hong, C. Patrick Yue
AverageLinear: Enhance Long-Term Time series forcasting with simple averaging
Gaoxiang Zhao, Li Zhou, Xiaoqiang Wang
Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction
Peixin Dai, Jingsi Zhang, Zhitao Shu
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions
Yash Agrawal, Srinidhi Balasubramanian, Rahul Meena, Rohail Alam, Himanshu Malviya, Rohini P
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
Weijie He, Tong Zhou, Yanlin Xiang, Yang Lin, Jiacheng Hu, Runyuan Bao
Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset
Suman Kunwar, Banji Raphael Owabumoye, Abayomi Simeon Alade
SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
Shengbo Tan, Rundong Xue, Shipeng Luo, Zeyu Zhang, Xinran Wang, Lei Zhang, Daji Ergu, Zhang Yi, Yang Zhao, Ying Cai