Continual learning requires learning incremental tasks with dynamic data
distributions. So far, it has been observed that employing a combination of
contrastive loss and distillation loss for training in continual learning
yields strong performance. To the best of our knowledge, however, this
contrastive continual learning framework lacks convincing theoretical
explanations. In this work, we fill this gap by establishing theoretical
performance guarantees, which reveal how the performance of the model is
bounded by training losses of previous tasks in the contrastive continual
learning framework. Our theoretical explanations further support the idea that
pre-training can benefit continual learning. Inspired by our theoretical
analysis of these guarantees, we propose a novel contrastive continual learning
algorithm called CILA, which uses adaptive distillation coefficients for
different tasks. These distillation coefficients are easily computed by the
ratio between average distillation losses and average contrastive losses from
previous tasks. Our method shows great improvement on standard benchmarks and
achieves new state-of-the-art performance.

通过对前一任务的训练损失进行理论分析建立了性能保证的理论解释并提出了一种新的自适应蒸馏系数的对比式连续学习算法 CILA，该算法在标准基准测试中取得了显著的改进和最新的最佳性能。