Capsule Networks provide a way of detecting parts of objects in an image and representing cognitive links between those parts. Which means that capsule networks in several poses will recognize the same purpose even though they haven’t seen the pose in the training data.

CNN’s work by accumulating feature sets at every layer. It starts by identifying points, then shapes, then actual objects. However, the information on spatial relationships of all these features is lost.

You can think of a CNN like this:

CNN = 2 eye’s + 1 Nose + 1 Mouth

CNN = Face

There are two eyes, one nose and one mouth, but something is wrong.

Can you spot it? We can quickly tell that there are an eye and a mouth in the wrong position and that this is not what a person should look. A well-trained CNN has trouble with this idea, though.

CNN apart from being easily tricked by images with features in the wrong place, when viewing an image in a different orientation, a CNN is also easily confused. One way to overcome this is by an intense preparation from all angles, but this takes a lot of time and too much of computational power.

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