All convolutional kernels of the same convolutional layer on a convolutional neural network share a weight. Is this statement true or false?

The statement is false because each convolutional kernel within the same convolutional layer has its own set of weights. In a convolutional neural network (CNN), a convolutional layer is designed to learn different features by applying multiple filters (or kernels) to the input data. Each filter is specialized in detecting specific visual patterns, such as edges, textures, or more complex patterns in later layers.

Each filter operates independently of the others, and their weights are adjusted during the training process to minimize the loss function. This independence allows the network to learn a diverse set of features from the input data, enhancing its ability to generalize and perform tasks like image recognition or classification effectively. The structure of CNNs with separate weights for each kernel is a foundational characteristic that supports their effectiveness in processing spatial hierarchies within the data.