  Continual learning requires incremental compatibility with a sequence of
tasks. However, the design of model architecture remains an open question: In
general, learning all tasks with a shared set of parameters suffers from severe
interference between tasks; while learning each task with a dedicated parameter
subspace is limited by scalability. In this work, we theoretically analyze the
generalization errors for learning plasticity and memory stability in continual
learning, which can be uniformly upper-bounded by (1) discrepancy between task
distributions, (2) flatness of loss landscape and (3) cover of parameter space.
Then, inspired by the robust biological learning system that processes
sequential experiences with multiple parallel compartments, we propose
Cooperation of Small Continual Learners (CoSCL) as a general strategy for
continual learning. Specifically, we present an architecture with a fixed
number of narrower sub-networks to learn all incremental tasks in parallel,
which can naturally reduce the two errors through improving the three
components of the upper bound. To strengthen this advantage, we encourage to
cooperate these sub-networks by penalizing the difference of predictions made
by their feature representations. With a fixed parameter budget, CoSCL can
improve a variety of representative continual learning approaches by a large
margin (e.g., up to 10.64% on CIFAR-100-SC, 9.33% on CIFAR-100-RS, 11.45% on
CUB-200-2011 and 6.72% on Tiny-ImageNet) and achieve the new state-of-the-art
performance.
