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4/ I've believed from the beginning of this project that the real "pay dirt" in deep learning that is still under explored is the discipline of sticking with the problems beyond scoring high on benchmarks and getting impressive cherry picked results.
5/ That is to say, making something real out of the models that can be reliably used by everyday users. This certainly has happened (style transfer and super-resolution are good examples), but not nearly as much has been done in this area as there is potential I think.
6/ Big reason I think is that, no matter your resources, it's a painfully difficult process and it takes a lot of time. Believe me, it's been so very tempting to give up. So video- it'll happen when it's ready. It'll take time. That's pretty much it.
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