work by S. Sabour, N. Frosst and G. Hinton, published in NIPS 2017.
Presented by Karel Ha.
A capsule is a group of neurons whose activity vector represents the
instantiation parameters of a specific type of entity such as an object or an
object part. We use the length of the activity vector to represent the
probability that the entity exists and its orientation to represent the
instantiation parameters. Active capsules at one level make predictions, via
transformation matrices, for the instantiation parameters of higher-level
capsules. When multiple predictions agree, a higher level capsule becomes
active. We show that a discrimininatively trained, multi-layer capsule system
achieves state-of-the-art performance on MNIST and is considerably better than
a convolutional net at recognizing highly overlapping digits. To achieve these
results we use an iterative routing-by-agreement mechanism: A lower-level
capsule prefers to send its output to higher level capsules whose activity
vectors have a big scalar product with the prediction coming from the
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