This makes me feel like neural networks are nothing but glorified hash functions. Well, if you think about it, here we are just optimizing for hash collisions of similar things.
The whole point of classification algorithms, including NNs, is to map similar inputs to similar outputs, or indeed the same output, and different images to different outputs. Hash functions usually attempt to erase all information of "similarity" between inputs. However, the metric that determines what "similar" means in a NN is not necessarily what we expect it to be.
But of course NNs are definitely just functions, in the strict mathematical sense. You could replace one with a large lookup table. The interesting part is the training: how to come up with the function in the first place.
But of course NNs are definitely just functions, in the strict mathematical sense. You could replace one with a large lookup table. The interesting part is the training: how to come up with the function in the first place.