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
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
Iman Abbasnejad, Fabio Zambetta, Flora Salim, Timothy Wiley, Jeffrey Chan, Russell Gallagher, Ehsan Abbasnejad
Dual-Stream Attention Transformers for Sewer Defect Classification
Abdullah Al Redwan Newaz, Mahdi Abdeldguerfi, Kendall N. Niles, Joe Tom
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review
Faruk Ahmed, Md. Taimur Ahad, Yousuf Rayhan Emon
1D-Convolutional transformer for Parkinson disease diagnosis from gait
Safwen Naimi, Wassim Bouachir, Guillaume-Alexandre Bilodeau
Weight-Sharing Regularization
Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks
Tom Kirstein, Simon Aßmann, Orkun Furat, Stefan Will, Volker Schmidt
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer
Javad Mirzapour Kaleybar, Hooman Saadat, Hooman Khaloo