channelwise

Channelwise, or channel-wise processing, refers to operations that act independently on each channel of a multi-channel tensor, such as a RGB image or a feature map in a neural network. In common data layouts, tensors have shape (N, C, H, W) and channelwise methods apply the same type of operation to every channel without mixing information across channels.

Examples of channelwise operations include channelwise convolution, also known as depthwise convolution, which applies a separate spatial filter to each input channel, producing an output with the same number of channels. When combined with a pointwise convolution, it forms depthwise separable convolutions, a design used to reduce computation and parameters in many architectures. Channelwise normalization computes statistics per channel, such as per-channel batch normalization, helping stabilize learning when channel scales vary. Channelwise attention reweights channels by generating a per-channel weight vector, often using global pooling to form channel descriptors and a small neural network to produce the weights, as in squeeze-and-excitation networks. Channelwise pooling operations, like global average pooling, collapse spatial dimensions independently for each channel, yielding a vector of length C.

Applications and impact: channelwise methods are central to efficient convolutional neural networks, particularly for mobile and edge devices. They enable processing that respects channel-specific information while reducing computational cost. Architectures such as Xception and MobileNets have popularized depthwise and other channelwise techniques, influencing subsequent designs in computer vision and related fields.