I'm reading up on Convolutions in neural nets, and they seem like a neat and efficient way of finding "features" in the input. But am I right in thinking that a high enough number of layers and neurons layers in a plain old multi-layered dense net would "find" the same features? Or have I missed something?
Are there any metrics or estimates available on the difference in training/prediction time and cpu/memory requirements between a network with a CNN layer and a plain DNN for the same accuracy?
Thanks in advance!