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
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems
Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari
A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic
Ioanna Gogou, Dimitrios Koutsomitropoulos
Deep Network Pruning: A Comparative Study on CNNs in Face Recognition
Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose Maria Buades Rubio, Prayag Tiwari, Josef Bigun
Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks
Amaan Aijaz Sheikh, Imaad Zaffar Khan
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling
Cristian Rodriguez-Opazo, Ehsan Abbasnejad, Damien Teney, Edison Marrese-Taylor, Hamed Damirchi, Anton van den Hengel
Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection
Filip Postepski, Grzegorz M. Wojcik, Krzysztof Wrobel, Andrzej Kawiak, Katarzyna Zemla, Grzegorz Sedek
Gamified AI Approch for Early Detection of Dementia
Paramita Kundu Maji, Soubhik Acharya, Priti Paul, Sanjay Chakraborty, Saikat Basu
Image Deraining with Frequency-Enhanced State Space Model
Shugo Yamashita, Masaaki Ikehara
Using a Convolutional Neural Network and Explainable AI to Diagnose Dementia Based on MRI Scans
Tyler Morris, Ziming Liu, Longjian Liu, Xiaopeng Zhao
Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing
Huanbai Liu, Fanlong Zhang, Yin Tan, Lian Huang, Yan Li, Guoheng Huang, Shenghong Luo, An Zeng
Apply Distributed CNN on Genomics to accelerate Transcription-Factor TAL1 Motif Prediction
Tasnim Assali, Zayneb Trabelsi Ayoub, Sofiane Ouni
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks
Parth Padalkar, Natalia Ślusarz, Ekaterina Komendantskaya, Gopal Gupta
Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration
Gao Tianci
NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
Saul Fuster, Umay Kiraz, Trygve Eftestøl, Emiel A. M. Janssen, Kjersti Engan
A Survey of Distributed Learning in Cloud, Mobile, and Edge Settings
Madison Threadgill, Andreas Gerstlauer
Hand bone age estimation using divide and conquer strategy and lightweight convolutional neural networks
Amin Ahmadi Kasani, Hedieh Sajedi
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields
Tom Fischer, Pascal Peter, Joachim Weickert, Eddy Ilg
Efficient Visual State Space Model for Image Deblurring
Lingshun Kong, Jiangxin Dong, Ming-Hsuan Yang, Jinshan Pan
Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks
Emily O. Garvin, Markus J. Bonse, Jean Hayoz, Gabriele Cugno, Jonas Spiller, Polychronis A. Patapis, Dominique Petit Dit de la Roche, Rakesh Nath-Ranga, Olivier Absil, Nicolai F. Meinshausen, Sascha P. Quanz
From CNNs to Transformers in Multimodal Human Action Recognition: A Survey
Muhammad Bilal Shaikh, Syed Mohammed Shamsul Islam, Douglas Chai, Naveed Akhtar