[_fh5n]

All these are definitely cool, but I think we're still a long way from leaving the "look at this cool toy" status and stepping into the "I can add value to society" status.

Furthermore, if we consider that most of these DL paper completely ignore the fact that the nets must run for days on a GPU to get decent results, then everything appears way less impressive. But that's just my opinion. I love working in deep learning, but we still have LOTS of work to do.

[iandanforth]

Could you elaborate? After running for days / weeks/ months the output is a net that can do inference in seconds, or with some now-common techniques milliseconds with only small reductions in accuracy. These nets can then be deployed to phones to solve a rapidly increasing number of identification tasks, everything from plants to cancer.

The time from theoretical paper to widely deployed app is smaller in DL than in any other field I have experience with.

[alook]

It's true that there aren't too many practical applications of GAN's yet, but I'd argue that transfer learning is already pretty powerful. It's fairly commonplace to approach a compute vision task by starting with VGG/AlexNet/etc and fine-tuning it on a relatively small dataset.

[rybosome]

There is a LOT of investment in model training right now, with frameworks, specialized hardware (like Google's TPU), cloud services, etc., not to mention the GPU vendors themselves scrabbling like mad to develop chipsets that accommodate this more efficiently.

It's going to take less and less time and money to train a useful model.