In convolutional neural networks, the convolution kernel needs to be randomly initialized. The purpose of random initialization is to enable each convolution kernel to learn different features.
The convolution kernel plays the role of extracting local features in the input data. It performs a convolution operation on the input data through a sliding window, extracts local features at different locations, and generates corresponding feature maps. These feature maps contain feature information at different abstract levels of the input data and have important representation capabilities.
Common convolution kernels include:
- Vertical Edge Detector: Used to detect vertical edges in pictures.
- Horizontal Edge Detector: Used to detect horizontal edges in pictures.
- Corner Detector: Used to detect corners and corners in pictures.
- Blur kernel: used to blur images.
- Sharpening Kernel: Used to enhance the contours and details of an image.
In addition to the common convolution kernels mentioned above, more complex and specialized convolution kernels can also be designed according to specific tasks and needs to adapt to various types of feature extraction tasks.