Fully Connected Layer vs Dense Layer in Image Processing

This study explores the distinction between Fully Connected (FC) and Dense layers in neural network-based image processing. Traditional FC layers from early deep learning models connect every neuron to all previous layer neurons, leading to high computational demands and increased risk of overfitting. Dense layers, a more refined form of FC layers, help lower parameter counts, enhancing efficiency in handling large-scale image data. The research examines how each layer type influences image classification performance metrics. Findings show Dense layers offer faster training and improved efficiency compared to general FC layers. However, FC layers often yield better outcomes in complex tasks requiring strong model expressiveness. The study highlights the trade-off between speed and accuracy in image-based deep learning, offering valuable insights for experienced developers.