<story><title>Understanding Hinton’s Capsule Networks, Part I: Intuition</title><url>https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b</url></story><parent_chain></parent_chain><comment><author>ilzmastr</author><text>&amp;quot;We have the face oval, two eyes, a nose and a mouth. For a CNN, a mere presence of these objects can be a very strong indicator to consider that there is a face in the image. Orientational and relative spatial relationships between these components are not very important to a CNN.&amp;quot;&lt;p&gt;^^^ What? The opposite of this is the mainstream I thought. The promise of DL is to learn hierarchical models of your data. The network learns edge filters, learns combinations of edge filters that differentiate an eye vs a nose, but doesn&amp;#x27;t learn combinations of intermediate features that determine a face? ppl usually say with a deep enough network an hierarchical concept can be learned...</text></comment>
<story><title>Understanding Hinton’s Capsule Networks, Part I: Intuition</title><url>https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b</url></story><parent_chain></parent_chain><comment><author>tmsldd</author><text>Hinton is with no doubt one of the greatest name in the field, but I think in this case particularly the paper fails to properly address citations. I have seen people publishing about dynamic routing and invariance problem by decades (e.g. C. Von der Malsburg, T. Poggio, and many others).
But I admit, authors in general name concepts in such obscure and convoluted way, which makes it very hard to actually separate contributions and give credits to whom deserves it.</text></comment>