Deep Convolutional Neural Network
Deep convolutional neural networks (CNNs) are a class of artificial neural networks designed to process data with a grid-like topology, such as images and videos, excelling at tasks like image classification, object detection, and segmentation. Current research focuses on improving CNN architectures (e.g., exploring variations of ResNet, Inception, and efficientNet models), developing novel training techniques (like integer-only training and self-knowledge distillation), and addressing challenges such as imbalanced datasets and catastrophic forgetting in incremental learning. The widespread application of CNNs across diverse fields, from medical image analysis and autonomous driving to agricultural monitoring and remote sensing, highlights their significant impact on both scientific understanding and practical problem-solving.
Papers
Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network
Nolann Lainé, Guillaume Zahnd, Herv é Liebgott, Maciej Orkisz
Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System
Zizhang Wu, Jason Wang, Tianhao Xu, Fan Wang
LAP: An Attention-Based Module for Concept Based Self-Interpretation and Knowledge Injection in Convolutional Neural Networks
Rassa Ghavami Modegh, Ahmad Salimi, Alireza Dizaji, Hamid R. Rabiee
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
Noam Razin, Asaf Maman, Nadav Cohen
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks
Dominik Müller, Iñaki Soto-Rey, Frank Kramer