  In a conventional supervised learning setting, a machine learning model has
access to examples of all object classes that are desired to be recognized
during the inference stage. This results in a fixed model that lacks the
flexibility to adapt to new learning tasks. In practical settings, learning
tasks often arrive in a sequence and the models must continually learn to
increment their previously acquired knowledge. Existing incremental learning
approaches fall well below the state-of-the-art cumulative models that use all
training classes at once. In this paper, we propose a random path selection
algorithm, called Adaptive RPS-Net, that progressively chooses optimal paths
for the new tasks while encouraging parameter sharing between tasks. We
introduce a new network capacity measure that enables us to automatically
switch paths if the already used resources are saturated. Since the proposed
path-reuse strategy ensures forward knowledge transfer, our approach is
efficient and has considerably less computation overhead. As an added novelty,
the proposed model integrates knowledge distillation and retrospection along
with the path selection strategy to overcome catastrophic forgetting. In order
to maintain an equilibrium between previous and newly acquired knowledge, we
propose a simple controller to dynamically balance the model plasticity.
Through extensive experiments, we demonstrate that the Adaptive RPS-Net method
surpasses the state-of-the-art performance for incremental learning and by
utilizing parallel computation this method can run in constant time with nearly
the same efficiency as a conventional deep convolutional neural network.
