I need help in understanding something basic.
In this video, Andrew Ng says, essentially, that convolutional layers are better than fully connected (FC) layers because they use fewer parameters.
But I'm having trouble seeing when FC layers would/could ever be used for what convolutional layers are used for, specifically, feature detection.
I always read that FC layers are used in the final, classification stages of a CNN, but could they ever be used for the feature detection part?
In other words, is it even possible for a "feature" to be deciphered when the filter size is the same as the entire image?
If not, it's hard for me to understand Andrew Ng's comparison---there aren't any parameter reduction "savings" if we're not going to use an FC "filter" in place of a CNN layer in the first place.
A semi-related question: Can multi-layer perceptrons (which I understand to be fully connected neural networks) be used for feature detection? If so, how do image-sized "filters" make any sense?