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
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
Ioannis Panopoulos, Stylianos I. Venieris, Iakovos S. Venieris
Recognition of Schrodinger cat state based on CNN
Tao Zhang, Chaoying Zhao
A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization
Deepak Dagar, Dinesh Kumar Vishwakarma
A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification
Kanthimathi S, Shravan Venkatraman, Jayasankar K S, Pranay Jiljith T, Jashwanth R
Self-Supervised Vision Transformers for Writer Retrieval
Tim Raven, Arthur Matei, Gernot A. Fink
PuYun: Medium-Range Global Weather Forecasting Using Large Kernel Attention Convolutional Networks
Shengchen Zhu, Yiming Chen, Peiying Yu, Xiang Qu, Yuxiao Zhou, Yiming Ma, Zhizhan Zhao, Yukai Liu, Hao Mi, Bin Wang
Statistical Analysis of the Impact of Quaternion Components in Convolutional Neural Networks
Gerardo Altamirano-Gómez, Carlos Gershenson
H-SGANet: Hybrid Sparse Graph Attention Network for Deformable Medical Image Registration
Yufeng Zhou, Wenming Cao
CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes
Rahul Khorana
Turbulence Strength $C_n^2$ Estimation from Video using Physics-based Deep Learning
Ripon Kumar Saha, Esen Salcin, Jihoo Kim, Joseph Smith, Suren Jayasuriya
CNN Based Detection of Cardiovascular Diseases from ECG Images
Irem Sayin, Rana Gursoy, Buse Cicek, Yunus Emre Mert, Fatih Ozturk, Taha Emre Pamukcu, Ceylin Deniz Sevimli, Huseyin Uvet
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator
Shiguang Wang, Tao Xie, Haijun Liu, Xingcheng Zhang, Jian Cheng
Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition
Axel Klawonn, Martin Lanser, Janine Weber
LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation
Trung Dinh Quoc Dang, Huy Hoang Nguyen, Aleksei Tiulpin
HAPM -- Hardware Aware Pruning Method for CNN hardware accelerators in resource constrained devices
Federico Nicolas Peccia, Luciano Ferreyro, Alejandro Furfaro
Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration
Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque