> "It works". Well that's sorta the point. Personally I'm not a super huge fan of creating a super specific network architecture and resulting in 2-3% differen

> "It works". Well that's sorta the point. Personally I'm not a super huge fan of creating a super specific network architecture and resulting in 2-3% difference in performance. Certainly if you're doing something where a configuration makes sense (LSTM for time series for example), but I think there needs to be a rethinking of the Grand Theory of Deep Learning Architecture TM. And frankly I think a unsaid reason why U-net is so popular is that it does generalize reasonably well with limited data, which in many fields is not as massive as COCO. I realize it's sorta asking too much (I both want a NN that works both out of the box, super easily, and doesn't require a TON of data), but I think that's where the current pains are for really explosive growth in AI.