 |
Let me give a simple explanation on the fundamental structure of CNN and the related functions. Generally, CNN includes three major layers: convolutional layer, pooling layer and fully-connected layer. The convolutional layer computes the output of neurons that are connected to local regions in the input matrix. All neurons compute a dot product between their weights and a small region through which they are connected to the input matrix. Such local regions can be defined as a filter with small width and height to reduce the computational complexity. The output of each filter is called a ``feature map'' which shows the effect of an edge check. Then the pooling layer performs a down-sampling operation along the spatial dimensions and places the data in a small volume. CNN can have several convolutional layers and pooling layers to dig the fundamental elements of image data. Finally, the fully-connected (FC) layer combines all fundamental elements of image data to provide the final decision. |
IEEE/ICACT20210223 Slide.09
[Big Slide]