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
Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach
Adam Wojciechowski, Mateusz Lango, Ondrej Dusek
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
Mirza Akhi Khatun, Mangolika Bhattacharya, Ciarán Eising, Lubna Luxmi Dhirani
Flexible graph convolutional network for 3D human pose estimation
Abu Taib Mohammed Shahjahan, A. Ben Hamza
Enhancing material property prediction with ensemble deep graph convolutional networks
Chowdhury Mohammad Abid Rahman, Ghadendra Bhandari, Nasser M Nasrabadi, Aldo H. Romero, Prashnna K. Gyawali
Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer
Carolin Flosdorf, Justin Engelker, Igor Keller, Nicolas Mohr
Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers
Zhengang Li, Alec Lu, Yanyue Xie, Zhenglun Kong, Mengshu Sun, Hao Tang, Zhong Jia Xue, Peiyan Dong, Caiwen Ding, Yanzhi Wang, Xue Lin, Zhenman Fang
CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation
Xiao Liu, Peng Gao, Tao Yu, Fei Wang, Ru-Yue Yuan
Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework
Ryosuke Wayama, Yuki Sasaki, Satoshi Kagiwada, Nobusuke Iwasaki, Hitoshi Iyatomi
Real Time American Sign Language Detection Using Yolo-v9
Amna Imran, Meghana Shashishekhara Hulikal, Hamza A. A. Gardi
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation
Saideep Kilaru, Kothamasu Jayachandra, Tanishka Yagneshwar, Suchi Kumari
Revising the Problem of Partial Labels from the Perspective of CNNs' Robustness
Xin Zhang, Yuqi Song, Wyatt McCurdy, Xiaofeng Wang, Fei Zuo
2D and 3D Deep Learning Models for MRI-based Parkinson's Disease Classification: A Comparative Analysis of Convolutional Kolmogorov-Arnold Networks, Convolutional Neural Networks, and Graph Convolutional Networks
Salil B Patel, Vicky Goh, James F FitzGerald, Chrystalina A Antoniades
Synthetic Trajectory Generation Through Convolutional Neural Networks
Jesse Merhi, Erik Buchholz, Salil S. Kanhere
Automatic Equalization for Individual Instrument Tracks Using Convolutional Neural Networks
Florian Mockenhaupt, Joscha Simon Rieber, Shahan Nercessian
DC is all you need: describing ReLU from a signal processing standpoint
Christodoulos Kechris, Jonathan Dan, Jose Miranda, David Atienza
Image Classification using Fuzzy Pooling in Convolutional Kolmogorov-Arnold Networks
Ayan Igali, Pakizar Shamoi
Pixel Embedding: Fully Quantized Convolutional Neural Network with Differentiable Lookup Table
Hiroyuki Tokunaga, Joel Nicholls, Daria Vazhenina, Atsunori Kanemura