Nowadays, convolutional neural networks perform very well in identifying objects, but unfortunately, they have very important problems that are very difficult to get rid of. Convolutional networks use multiple layers of feature detectors. Each feature detector is local, so feature detectors are repeated across the space. Pooling gives some translational invariance in much deeper layers, but only in a crude way. The psychology of shape perception suggests that the human brain achieves translational invariance in a much better way. We know that, roughly speaking, the brain has two separate pathways, a “from” and “where”. Neurons in the “from” pathway respond to a particular type of stimulus regardless of where it is in the visual field. Neurons “where” are responsible for encoding where things are. As a side note, it is hypothesized that the “from” has a lower resolution then “where”.
