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
Flexible Coded Distributed Convolution Computing for Enhanced Fault Tolerance and Numerical Stability in Distributed CNNs
Shuo Tan, Rui Liu, XianLei Long, Kai Wan, Linqi Song, Yong Li
Exploring PCA-based feature representations of image pixels via CNN to enhance food image segmentation
Ying Dai
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai, Hui Chen, Guochu Xiong, Weichen Liu
Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks
Aarjav Kavathia, Simeon Sayer
Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures
Ameya Uppina, S Navaneetha Krishnan, Talluri Krishna Sai Teja, Nikhil N Iyer, Joe Dhanith P R
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
Shreyash Arya, Sukrut Rao, Moritz Böhle, Bernt Schiele
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
Yuyan Zhang, Derya Soydaner, Fatemeh Behrad, Lisa Koßmann, Johan Wagemans
Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach
Marjan Qazvini
Inducing Semi-Structured Sparsity by Masking for Efficient Model Inference in Convolutional Networks
David A. Danhofer
Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia
Ibne Hassan, Aman Mujahid, Abdullah Al Hasib, Andalib Rahman Shagoto, Joyanta Jyoti Mondal, Meem Arafat Manab, Jannatun Noor
Advanced Predictive Quality Assessment for Ultrasonic Additive Manufacturing with Deep Learning Model
Lokendra Poudel, Sushant Jha, Ryan Meeker, Duy-Nhat Phan, Rahul Bhowmik
Improving snore detection under limited dataset through harmonic/percussive source separation and convolutional neural networks
F.D. Gonzalez-Martinez, J.J. Carabias-Orti, F.J. Canadas-Quesada, N. Ruiz-Reyes, D. Martinez-Munoz, S. Garcia-Galan
Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction
Guan-Hua Huang, Wan-Chen Lai, Tai-Been Chen, Chien-Chin Hsu, Huei-Yung Chen, Yi-Chen Wu, Li-Ren Yeh
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks
Axel Klawonn, Martin Lanser, Janine Weber
ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
Junjie Ni, Guofeng Zhang, Guanglin Li, Yijin Li, Xinyang Liu, Zhaoyang Huang, Hujun Bao
FilterViT and DropoutViT: Lightweight Vision Transformer Models for Efficient Attention Mechanisms
Bohang Sun (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China)
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks
Joseph Damilola Akinyemi, Kolawole John Adebayo
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm
Zaniar Sharifi, Khabat Soltanian, Ali Amiri
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
Patricia Pauli, Ruigang Wang, Ian Manchester, Frank Allgöwer