While the inductive biases of locality and translation invariance impose severe restrictions on the convolutional kernel, drastically reducing its parameter count, adding multiple channels allows the network to recover some of this lost expressive power. By utilizing multiple feature channels, the model regains the complexity necessary to capture rich, varied representations from the data without sacrificing the computational benefits of these inductive biases.