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I predict capsule networks will not nearly have as big of an impact on the ML community as many think it will. Why? Because the main reason they exist is to add
by tw1010 9y ago
I predict capsule networks will not nearly have as big of an impact on the ML community as many think it will. Why? Because the main reason they exist is to address performance issues in really advanced, cutting-edge, models. But that is not what drives upvotes here and on reddit. The failure of capsule networks to pick up steam and the continued popularity of GANs, I think, is a signal that the main reason ML is still trendy and in vogue is because the subject, AI, tickles the imagination of engineers, but is still, five years after ML started to become popular, not as big in actual practical engineering systems as what the outside public might think it is.
- Mmrnmhrm 9y agoI agree, it still needs a strong use case. IMHO, new network architectures are generally overhyped. Even AlexNet performs wonderfully well in most problems once you add nice initialization and batch normalization.
- chillee 9y agoI think you have a misconception of what capsule networks are. They are not intended to address "performance issues in really advanced models", they are intended as another paradigm in deep learning that Geoff Hinton thinks has a lot of promise. I also don't know what you mean by "the failure of capsule networks to pick up steam". The paper literally came out a month ago. It's too early to say whether it'll "pick up steam" or not. I also don't understand what you mean by "the continued popularity of GANs" showing anything.
- dnautics 9y agoThe specific performance issue they are designed to address is training set size dependence. That's not exactly trivial.