  We propose a novel approach to lifelong learning, introducing a compact
encapsulated support structure which endows a network with the capability to
expand its capacity as needed to learn new tasks while preventing the loss of
learned tasks. This is achieved by splitting neurons with high semantic drift
and constructing an adjacent network to encode the new tasks at hand. We call
this the Plastic Support Structure (PSS), it is a compact structure to learn
new tasks that cannot be efficiently encoded in the existing structure of the
network. We validate the PSS on public datasets against existing lifelong
learning architectures, showing it performs similarly to them but without prior
knowledge of the task and in some cases with fewer parameters and in a more
understandable fashion where the PSS is an encapsulated container for specific
features related to specific tasks, thus making it an ideal "add-on" solution
for endowing a network to learn more tasks.

Machine Learning