Let us emphasize, though, that the receptive field size is not the only factor contributing to the improved performance mentioned above. Other factors play a very important role: network depth (i.e., number of layers) and width (i.e., number of filters per layer), residual connections, batch normalization, to name only a few. In other words, while we conjecture that a large receptive field is necessary, by no means it is sufficient. Additional experimentation is needed to confirm this hypothesis: for example, researchers may experimentally investigate how classification accuracy changes as kernel sizes and strides vary for different architectures. This may indicate if, at least for those architectures, a large receptive field is necessary.