Deep CV: Advanced Convolutional Layers

In the world of Deep Computer Vision, there are several types of convolutional layers that differ from the original convolutional layer which was discussed in the previous Deep CV tutorial. These layers are used in many popular advanced convolutional neural network implementations found in the Deep Learning research side of Computer Vision. Each of these layers has a different mechanism than the original convolutional layer and this allows each type of layer to have a particularly special function.

Before getting into these advanced convolutional layers, let’s first have a quick recap on how the original convolutional layer works.

Original Convolutional Layer

In the original convolutional layer, we have an input that has a shape (W*H*C) where W and H are the width and height of each feature map and C is the number of channels, which is basically the total number of feature maps. The convolutional layer will have a certain number of kernels which will perform the convolution operation on this input. The number of kernels will be equal to the number of desired channels in the output feature map. Basically, each kernel will correspond to a particular feature map in the output and each feature map is a channel.

The height and width of the kernel is something that we decide, and usually, we keep it as 3*3. The depth of each kernel will be equal to the number of channels of the input. Hence for the below example, the shape of each kernel will be (w*h*3)…