  Exemplar-free Class Incremental Learning (EFCIL) aims to sequentially learn
tasks with access only to data from the current one. EFCIL is of interest
because it mitigates concerns about privacy and long-term storage of data,
while at the same time alleviating the problem of catastrophic forgetting in
incremental learning. In this work, we introduce task-adaptive saliency for
EFCIL and propose a new framework, which we call Task-Adaptive Saliency
Supervision (TASS), for mitigating the negative effects of saliency drift
between different tasks. We first apply boundary-guided saliency to maintain
task adaptivity and \textit{plasticity} on model attention. Besides, we
introduce task-agnostic low-level signals as auxiliary supervision to increase
the \textit{stability} of model attention. Finally, we introduce a module for
injecting and recovering saliency noise to increase the robustness of saliency
preservation. Our experiments demonstrate that our method can better preserve
saliency maps across tasks and achieve state-of-the-art results on the
CIFAR-100, Tiny-ImageNet, and ImageNet-Subset EFCIL benchmarks. Code is
available at \url{https://github.com/scok30/tass}.
