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
5G NR PRACH Detection with Convolutional Neural Networks (CNN): Overcoming Cell Interference Challenges
Desire Guel, Arsene Kabore, Didier Bassole
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation
Yaowen Bi, Yuteng Lian, Jie Cui, Jun Liu, Peijian Wang, Guanghui Li, Xuejun Chen, Jinglin Zhao, Hao Wen, Jing Zhang, Zhaoqi Zhang, Wenzhuo Song, Yang Sun, Weiwei Zhang, Mingchen Cai, Jian Dong, Guanxing Zhang
CNN-based Labelled Crack Detection for Image Annotation
Mohsen Asghari Ilani, Leila Amini, Hossein Karimi, Maryam Shavali Kuhshuri
Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond
Chiara Stuardi, Claudio Gheller, Franco Vazza, Andrea Botteon
Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model
Aliza Subedi, Smriti Regmi, Nisha Regmi, Bhumi Bhusal, Ulas Bagci, Debesh Jha
A Tutorial on Explainable Image Classification for Dementia Stages Using Convolutional Neural Network and Gradient-weighted Class Activation Mapping
Kevin Kam Fung Yuen
AI and Entrepreneurship: Facial Recognition Technology Detects Entrepreneurs, Outperforming Human Experts
Martin Obschonka, Christian Fisch, Tharindu Fernando, Clinton Fookes
No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data
Christopher Klugmann, Rafid Mahmood, Guruprasad Hegde, Amit Kale, Daniel Kondermann
Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection
Ik Jun Moon, Junho Moon, Ikbeom Jang
PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile Applications
Kshitij Bhardwaj
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace
John Oyekan, Liam Quantrill, Christopher Turner, Ashutosh Tiwari
Training Large-Scale Optical Neural Networks with Two-Pass Forward Propagation
Amirreza Ahmadnejad, Somayyeh Koohi
Algebraic Representations for Faster Predictions in Convolutional Neural Networks
Johnny Joyce, Jan Verschelde
Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data
J Shepard Bryan, Meyam Tavakoli, Steve Presse
CNN-JEPA: Self-Supervised Pretraining Convolutional Neural Networks Using Joint Embedding Predictive Architecture
András Kalapos, Bálint Gyires-Tóth