In this work, we investigate exemplar-free class incremental learning (CIL)
with knowledge distillation (KD) as a regularization strategy, aiming to
prevent forgetting. KD-based methods are successfully used in CIL, but they
often struggle to regularize the model without access to exemplars of the
training data from previous tasks. Our analysis reveals that this issue
originates from substantial representation shifts in the teacher network when
dealing with out-of-distribution data. This causes large errors in the KD loss
component, leading to performance degradation in CIL. Inspired by recent
test-time adaptation methods, we introduce Teacher Adaptation (TA), a method
that concurrently updates the teacher and the main model during incremental
training. Our method seamlessly integrates with KD-based CIL approaches and
allows for consistent enhancement of their performance across multiple
exemplar-free CIL benchmarks.

通过知识蒸馏作为正则化策略，我们研究了不需要示例的类别增量学习（CIL），以防止遗忘。我们引入了一种名为教师适应（TA）的方法，它与基于知识蒸馏的 CIL 方法无缝结合，并在多个不需要示例的 CIL 基准测试中持续提高性能。