Our experience led us to some interesting conclusions which lay out great hopes in the near future.
- Even the most naively designed DCNN did better to eliminate selfies than a custom hand tuned face detector into which multiple researcher-decades have been invested! This bodes well for using a neural compute architecture as a default building block for such “intelligence” tasks.
- Getting a network to generalize well is hard in artistic domains. This is because the training data isn’t clear cut. Humans too can’t quite tell the difference between art and junk with good consensus. Since ours is an artistic domain, our system needs to face the same uncertainty. So we bias in favour of Fontli network members, so that genuine interesting posts are not inadvertently marked as “spam”.
- The compute is the easy part today with very high level expressive libraries like Keras being available. Most of our work was with preparing the data and monitoring the adequacy of the data set.
- If you’re going to experiment using DCNNs, do yourself a favour and get a fairly powerful desktop computer with a good NVidia graphics card - at least a GTX 1080. The now launched GTX 1080 Ti looks promising. The more cards, the merrier.