Compared with the standard neural nets using a single scalar (the output of a neural unit) to represent the detected semantics, the vectorized rep-resentation in capsule network enables us to utilize hyperplanes to extract the component from the overall representation and get the specific sense.
we propose to attach different hyperplanes to capsules to tackle the ambiguity caused by polysemy.