[real-hacker]

It looks RCN sits between traditional machine learning (with manual feature selection) and 'modern' neural networks (CNN). The traditional methods are too rigid to capture the essential information, while the CNNs sometimes are too flexible to avoid overfitting. Different from CNNS, RCNs have a predetermined structure. Humans are not born a blank slate, we have a neural structure encoded in our genes, so we don't need millions of training samples to recognize objects. So maybe RCN is onto something.

I am curious how RCN performs on real-life images like ImageNet, and how do they perform against adversarial examples. If they can easily recognize adversarial examples, that would be very interesting...