Just as input images are represented as third-order tensors, hidden representations in convolutional neural networks are also formulated as third-order tensors, denoted as $\mathsf{H}$. Instead of a single value per spatial location, there is an entire vector of hidden representations for each location. These representations can be conceptualized as multiple two-dimensional grids stacked together, commonly referred to as channels or feature maps, where lower layers might recognize basic features like edges and deeper layers capture complex textures.