Reading the ResNet paper, paragraph 3.3:The convolutional layers mostly have 33 filters and follow two simple  design rules: (i) for the same output feature map size, the layers  have the same number of filters; and (ii) if the feature map size is  halved, the number of filters is doubled so as to preserve the time  complexity per layer.The network uses convolutional layers with stride 2 to halve the feature map size, but this divides both the width and the height of the feature maps, so the total area is divided by 4, not by 2.So I am guessing the time complexity per layer is halved every time the feature maps are halved. Did I miss something?
Do all layers have the same computational complexity in a ResNet?
convnet