mCNNs
mCNNs, also known as multilayer convolutional neural networks, refer to a class of deep learning models that build upon the architecture of traditional CNNs. These models are designed to learn more complex and abstract features from input data through multiple layers of convolutional and pooling operations.
The primary difference between mCNNs and traditional CNNs is the hierarchical representation learning capability, which allows
The multilayer architecture of mCNNs consists of multiple convolutional and pooling layers, which are typically followed
mCNNs have been widely adopted in various fields, including computer vision, natural language processing, and signal