Vanilla Convolutional Neural Network

Vanilla convolutional neural networks (CNNs) represent a fundamental architecture in deep learning, focusing on understanding their inherent properties and improving their training efficiency without relying on architectural shortcuts like residual connections or normalization layers. Current research explores their application in diverse fields, from image classification and object detection in medical imaging to location identification and multi-view stereo reconstruction, often comparing their performance against more complex architectures. This research is significant because it clarifies the theoretical underpinnings of CNNs and potentially leads to more efficient and robust models for various applications by optimizing training strategies and activation functions.