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
Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
Giovanni Messuti, ortensia Amoroso, Ferdinando Napolitano, Mariarosaria Falanga, Paolo Capuano, Silvia Scarpetta
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
Xuanchen (Willow)Liu, Shuxin Qiao, Kyle Gao, Hongjie He, Michael A. Chapman, Linlin Xu, Jonathan Li
Convolutional neural networks applied to modification of images
Carlos I. Aguirre-Velez, Jose Antonio Arciniega-Nevarez, Eric Dolores-Cuenca
Residual Kolmogorov-Arnold Network for Enhanced Deep Learning
Ray Congrui Yu, Sherry Wu, Jiang Gui
AI-Driven Early Mental Health Screening with Limited Data: Analyzing Selfies of Pregnant Women
Gustavo A. Basílio, Thiago B. Pereira, Alessandro L. Koerich, Ludmila Dias, Maria das Graças da S. Teixeira, Rafael T. Sousa, Wilian H. Hisatugu, Amanda S. Mota, Anilton S. Garcia, Marco Aurélio K. Galletta, Hermano Tavares, Thiago M. Paixão
Art Forgery Detection using Kolmogorov Arnold and Convolutional Neural Networks
Sandro Boccuzzo, Deborah Desirée Meyer, Ludovica Schaerf
Radio Map Prediction from Aerial Images and Application to Coverage Optimization
Fabian Jaensch, Giuseppe Caire, Begüm Demir
Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data
Matteo Risso, Alessia Goffi, Beatrice Alessandra Motetti, Alessio Burrello, Jean Baptiste Bove, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari, Giuseppe Maffeis
A Brain-Inspired Regularizer for Adversarial Robustness
Elie Attias, Cengiz Pehlevan, Dina Obeid
Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification
Gary Murphy, Raghubir Singh
Mamba in Vision: A Comprehensive Survey of Techniques and Applications
Md Maklachur Rahman, Abdullah Aman Tutul, Ankur Nath, Lamyanba Laishram, Soon Ki Jung, Tracy Hammond
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, Nagendra Kumar
Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network
Yangyang Qiu, Guoan Xu, Guangwei Gao, Zhenhua Guo, Yi Yu, Chia-Wen Lin
Impact of White-Box Adversarial Attacks on Convolutional Neural Networks
Rakesh Podder, Sudipto Ghosh
Resource-efficient equivariant quantum convolutional neural networks
Koki Chinzei, Quoc Hoan Tran, Yasuhiro Endo, Hirotaka Oshima
RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis
Saddam Hussain Khan, Rashid Iqbal (Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan)
[Re] Network Deconvolution
Rochana R. Obadage, Kumushini Thennakoon, Sarah M. Rajtmajer, Jian Wu