Let's compare a 7x7 kernel with three iterations of 3x3 kernels. For this example, we will pretend each layer is only one channel deep and that no activations functions are applied in between. Yes, it's horribly oversimplified, but bear with me. read more
Let's compare a 7x7 kernel with three iterations of 3x3 kernels. For this example, we will pretend each layer is only one channel deep and that no activations functions are applied in between. read more
However conventional kernel size's are 3x3, 5x5 and 7x7. A well known architecture for classification is to use convolution pooling, convolution pooling etc. and some fully connected layers on top. Just start of with a modest number of layers and increase the number while measuring you performance on the test set. read more
This is the first crucial point to understand: Traditionally people have designed kernels, but in Deep Learning, we let the network decide what the best kernel should be. The one thing we do specify however, is the kernel dimensions. (This is called a hyperparameter, for example, 5x5, or 3x3, etc). read more