By sparse images, I mean images where each R, G, B value is either 0 or 1. Does this contribute in faster training or any other process of NN training? My guess would be that having multiple nodes multiplied by 0s (and consequently being dropped) would help the model run faster.
Is this true? Or maybe large sparse matrices help in a different way? And how could I observe/measure it?
