  Contrastive representation learning has emerged as a promising technique for
continual learning as it can learn representations that are robust to
catastrophic forgetting and generalize well to unseen future tasks. Previous
work in continual learning has addressed forgetting by using previous task data
and trained models. Inspired by event models created and updated in the brain,
we propose a new mechanism that takes place during task boundaries, i.e., when
one task finishes and another starts. By observing the redundancy-inducing
ability of contrastive loss on the output of a neural network, our method
leverages the first few samples of the new task to identify and retain
parameters contributing most to the transfer ability of the neural network,
freeing up the remaining parts of the network to learn new features. We
evaluate the proposed methods on benchmark computer vision datasets including
CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the
task-incremental, class-incremental, and domain-incremental continual learning
scenarios.
