  Most existing works on continual learning (CL) focus on overcoming the
catastrophic forgetting (CF) problem, with dynamic models and replay methods
performing exceptionally well. However, since current works tend to assume
exclusivity or dissimilarity among learning tasks, these methods require
constantly accumulating task-specific knowledge in memory for each task. This
results in the eventual prohibitive expansion of the knowledge repository if we
consider learning from a long sequence of tasks. In this work, we introduce a
paradigm where the continual learner gets a sequence of mixed similar and
dissimilar tasks. We propose a new continual learning framework that uses a
task similarity detection function that does not require additional learning,
with which we analyze whether there is a specific task in the past that is
similar to the current task. We can then reuse previous task knowledge to slow
down parameter expansion, ensuring that the CL system expands the knowledge
repository sublinearly to the number of learned tasks. Our experiments show
that the proposed framework performs competitively on widely used computer
vision benchmarks such as CIFAR10, CIFAR100, and EMNIST.
