Capsule Networks (CapsNet) are recently proposed multi-stage computational models specialized for entity representation and discovery in image data. CapsNet employs iterative routing that shapes how the information cascades through different levels of interpretations. In this work, we investigate i) how the routing affects the CapsNet model fitting, ii) how the representation by capsules helps discover global structures in data distribution and iii) how learned data representation adapts and generalizes to new tasks. Our investigation shows: i) routing operation determines the certainty with which one layer of capsules pass information to the layer above, and the appropriate level of certainty is related to the model fitness, ii) in a designed experiment using data with a known 2D structure, capsule representations allow more meaningful 2D manifold embedding than neurons in a standard CNN do and iii) compared to neurons of standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution. Corresponding author, email: mazy@gpnu.edu.cn 4 To be more specific, the "atomic units" refer to the basic random variables that we are concerned with. In practical image/video analytic tasks, this concept of lower "convolutional" layers correspond to an element of a channel at a particular image location.
Lin et al. REF showed that capsule representations learn more meaningful 2D manifold embeddings than neurons in a standard CNN do.
On Learning and Learned Representation with Dynamic Routing in Capsule Networks
IEEE Access, vol. 7, pp. 50808-50822, 2019
Computer Science