Given an input volume of shape $(10,10,3),$ you consider using one of the two following layers:

Fully$-$connected layer with $2$ neurons, with biases

Convolutional layer with three $2$x$2$ filters $($with biases$)$ with $0$ padding and a stride of $2$

If you use the fully$-$connected layer, the input volume is flattened into a column vector before being

fed into the layer. What is the difference in the number of trainable parameters between these two layers?

$($A$)$ The fully$-$connected layer has $566$ fewer parameters

$($B$)$ The convolutional layer has $566$ fewer parameters

$($C$)$ The convolutional layer has $564$ fewer parameters

$($D$)$ The convolutional layer has $563$ fewer parameters