Maybe I'm missing something, but from the paper https://www.cs.toronto.edu/~hinton/FFA13.pdf, they use non-conv nets on CIFAR-10 for back prop, resulting in 63% accuracy. And FF achieves 59% accuracy (at best).
Those are relatively close figures, but good accuracy on CIFAR-10 is 99%+ and getting ~94% is trivial.
So, if an improper architecture for a problem is used and the accuracy is poor, how compelling is using another optimization approach and achieving similar accuracy?
It's a unique and interesting approach, but the article specifically mentions it gets accuracy similar to backprop, but if this is the experiment that claim is based on, it loses some credibility in my eyes.
Those are relatively close figures, but good accuracy on CIFAR-10 is 99%+ and getting ~94% is trivial.
So, if an improper architecture for a problem is used and the accuracy is poor, how compelling is using another optimization approach and achieving similar accuracy?
It's a unique and interesting approach, but the article specifically mentions it gets accuracy similar to backprop, but if this is the experiment that claim is based on, it loses some credibility in my eyes.