The answer is False.
A convolutional kernel is a small matrix of weights that is used to perform a convolution operation on an input image. The size of the kernel determines the size of the receptive field of the neuron, which is the region of the input image that the neuron is sensitive to. A larger receptive field allows the neuron to capture more information from the input image, but it also makes the neuron more computationally expensive to train.
Increasing the size of a convolutional kernel does not necessarily increase the performance of a convolutional network. In fact, it can sometimes lead to worse performance, because it can make the network more prone to overfitting. Overfitting occurs when a network learns the training data too well and is unable to generalize to new data.
The optimal size of a convolutional kernel depends on the specific task that the network is being trained to perform. For example, a network that is being trained to classify images of cats and dogs may need a larger receptive field than a network that is being trained to classify handwritten digits.
In general, it is important to experiment with different kernel sizes to find the one that works best for a particular task.