Math needs to fulfill a purpose in order to succeed in INDUSTRY. A image recognition is first and foremost about recognizing an image, the math behind it is mys

Math needs to fulfill a purpose in order to succeed in INDUSTRY. A image recognition is first and foremost about recognizing an image, the math behind it is mystical, but the accuracy is measurable. If you want your knowledge to be hold as useful, then you need find a usage case for it. Simple as that. This is not only true for math, but for all the other techniques as well. Otherwise so-called knowledge is yet another self-indulgent toy, disconnected even further from being useful. Contrary to what OP states here, recent development of WGAN and LSGAN pretty much math driven, and it leads to very useful realworld extension to the original model, that improves it quite a bit.