  Lifelong machine learning or continual learning models attempt to learn
incrementally by accumulating knowledge across a sequence of tasks. Therefore,
these models learn better and faster. They are used in various intelligent
systems that have to interact with humans or any dynamic environment e.g.,
chatbots and self-driving cars. Memory-less approach is more often used with
deep neural networks that accommodates incoming information from tasks within
its architecture. It allows them to perform well on all the seen tasks. These
models suffer from semantic drift or the plasticity-stability dilemma. The
existing models use Minkowski distance measures to decide which nodes to
freeze, update or duplicate. These distance metrics do not provide better
separation of nodes as they are susceptible to high dimensional sparse vectors.
In our proposed approach, we use angular distance to evaluate the semantic
drift in individual nodes that provide better separation of nodes and thus
better balancing between stability and plasticity. The proposed approach
outperforms state-of-the art models by maintaining higher accuracy on standard
datasets.
