*2.5. Convolutional Layers*

Each convolutional layer is composed of several graphic filters called kernels, it works just like the way in image processing does. Through convolutions, the kernels enhance part of the image's characteristic and turn the image into an individual feature map. The feature maps are all the same size and are bundled together to become a brand-new image. The convolutional layer provides an example regarding what the new image will look like. Each map in the same image is called a *channel*, and the number of channels becomes the depth of the image. When working through the convolution layers, the kernel actually processes all the channels once at a time. Another important aspect of the convolutional layer is *parameter sharing*. If we look back to the processing method of MLP, we can discover that each pixel in the same image needs to be applied to di fferent kernels. However, in convolutional layers, the whole image shares the same kernel to create a feature map, which gives CNN an important *shift invariant* characteristic. As the kernel can move all around the image, the features correlated with the kernel can be detected anywhere, which gives CNN superior performance in image recognition.
