A Complete Neural Network-Based Representation of High-Dimension Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a highly used machine learning architecture in various fields. Typical descriptions of CNNs are based on low-dimension and tensor representations in the feature extraction part. In this article, we extend the setting of CNNs to any arbitrary dimension and linearize the whole setting via the typical layers of neurons. In essence, a partial and a full network construct the entire process of a standard CNN, with the partial network being used to linearize the feature extraction. By doing so, we link the tensor-style representation of CNNs with the pure network representation. The outcomes serve two main purposes: to relate CNNs with other machine learning frameworks and to facilitate intuitive representations.