  Class incremental learning (CIL) aims to recognize both the old and new
classes along the increment tasks. Deep neural networks in CIL suffer from
catastrophic forgetting and some approaches rely on saving exemplars from
previous tasks, known as the exemplar-based setting, to alleviate this problem.
On the contrary, this paper focuses on the Exemplar-Free setting with no old
class sample preserved. Balancing the plasticity and stability in deep feature
learning with only supervision from new classes is more challenging. Most
existing Exemplar-Free CIL methods report the overall performance only and lack
further analysis. In this work, different methods are examined with
complementary metrics in greater detail. Moreover, we propose a simple CIL
method, Rotation Augmented Distillation (RAD), which achieves one of the
top-tier performances under the Exemplar-Free setting. Detailed analysis shows
our RAD benefits from the superior balance between plasticity and stability.
Finally, more challenging exemplar-free settings with fewer initial classes are
undertaken for further demonstrations and comparisons among the
state-of-the-art methods.
