Saleh Albashrawi

In a typical CNN model, the weight matrix represents the learnable parameters within each convolutional filter (kernel). These filters move over the input image or feature map, capturing relevant features. The weights of each filter determine its interaction with the input data. For example, a 3x3 filter might detect edges, while a 5x5 filter could identify textures. Researchers experiment with different filter sizes and configurations to optimize performance for specific tasks.

The relationship between weight matrices and kernels is crucial. Kernels are the sliding windows that move across the input during convolution. Each kernel corresponds to a weight matrix, and the dot product between the weights and the local receptive field generates the output feature map. By learning appropriate weights, the CNN adapts to the task and learns to recognize relevant patterns in the data.

Thank you a lot. So, we can say that we have two things: Weight matrix and kernels and these two are different but have some relation. I am right?