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
Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability
Dimitris Bertsimas, Lisa Everest, Jiayi Gu, Matthew Peroni, Vasiliki Stoumpou
Knowledge Distillation for Real-Time Classification of Early Media in Voice Communications
Kemal Altwlkany, Hadžem Hadžić, Amar Kurić, Emanuel Lacic
Enhancing Learned Image Compression via Cross Window-based Attention
Priyanka Mudgal, Feng Liu
Computable Lipschitz Bounds for Deep Neural Networks
Moreno Pintore, Bruno Després
SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity
Kunyun Wang, Jieru Zhao, Shuo Yang, Wenchao Ding, Minyi Guo
Automated Defect Detection and Grading of Piarom Dates Using Deep Learning
Nasrin Azimi, Danial Mohammad Rezaei
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture
Ach. Khozaimi, Wayan Firdaus Mahmudy
Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
Arrun Sivasubramanian, Divya Sasidharan, Sowmya V, Vinayakumar Ravi
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup, David Dobson, Kendall N. Niles, Ken Pathak, Steven Sloan
Leaky ReLUs That Differ in Forward and Backward Pass Facilitate Activation Maximization in Deep Neural Networks
Christoph Linse, Erhardt Barth, Thomas Martinetz
Enhancing Generalization in Convolutional Neural Networks through Regularization with Edge and Line Features
Christoph Linse, Beatrice Brückner, Thomas Martinetz
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification
Ganga Prasad Basyal, David Zeng, Bhaskar Pm Rimal
Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
Suman Sapkota
Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
Nikos Sakellariou (1), Antonios Lalas (1), Konstantinos Votis (1), Dimitrios Tzovaras (1) ((1) Centre for Research and Technology Hellas, Information Technologies Institute)
FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation
Sadjad Rezvani, Mansoor Fateh, Yeganeh Jalali, Amirreza Fateh