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
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification
Yannick Emonds, Kai Xi, Holger Fröning
Interpreting and Improving Attention From the Perspective of Large Kernel Convolution
Chenghao Li, Chaoning Zhang, Boheng Zeng, Yi Lu, Pengbo Shi, Qingzi Chen, Jirui Liu, Lingyun Zhu, Yang Yang, Heng Tao Shen
Self Expanding Convolutional Neural Networks
Blaise Appolinary, Alex Deaconu, Sophia Yang, Qingze, Li
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Jun Ma, Feifei Li, Bo Wang
CoordGate: Efficiently Computing Spatially-Varying Convolutions in Convolutional Neural Networks
Sunny Howard, Peter Norreys, Andreas Döpp
Convolutional Neural Network Ensemble Learning for Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment
Chollette C. Olisah, Ben Trewhella, Bo Li, Melvyn L. Smith, Benjamin Winstone, E. Charles Whitfield, Felicidad Fernández Fernández, Harriet Duncalfe
Kronecker Product Feature Fusion for Convolutional Neural Network in Remote Sensing Scene Classification
Yinzhu Cheng
Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes
Abdel Rahman Alsabbagh, Omar Al-Kadi
A Primer on Temporal Graph Learning
Aniq Ur Rahman, Justin P. Coon
Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations
Paul Brimoh, Chollette C. Olisah
Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction
Manoj S H, Arya A Bosale
A Random Ensemble of Encrypted models for Enhancing Robustness against Adversarial Examples
Ryota Iijima, Sayaka Shiota, Hitoshi Kiya
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy
Pegah Ahadian, Maryam Babaei, Kourosh Parand
A novel method to enhance pneumonia detection via a model-level ensembling of CNN and vision transformer
Sandeep Angara, Nishith Reddy Mannuru, Aashrith Mannuru, Sharath Thirunagaru