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.

本研究关注在没有保存旧类样本（无样例）的无样例设置下，通过只对新类进行监督，实现在深层特征学习中平衡可塑性和稳定性。通过对不同方法的详细对比及指标衡量，提出了一种简单的无样例设置下的 Class incremental learning 方法，即 Rotation Augmented Distillation (RAD)，其表现接近顶级水平。此外，在更具挑战性的无样例设置下，进行了更多的实证和与现有方法的比较。

旋转增强蒸馏的免样例类增量学习与详细分析

Rotation Augmented Distillation for Exemplar-Free Class Incremental  Learning with Detailed Analysis

In this work, we study the continual semantic segmentation problem, where the
deep neural networks are required to incorporate new classes continually
without catastrophic forgetting. We propose to use a structural
re-parameterization mechanism, named representation compensation (RC) module,
to decouple the representation learning of both old and new knowledge. The RC
module consists of two dynamically evolved branches with one frozen and one
trainable. Besides, we design a pooled cube knowledge distillation strategy on
both spatial and channel dimensions to further enhance the plasticity and
stability of the model. We conduct experiments on two challenging continual
semantic segmentation scenarios, continual class segmentation and continual
domain segmentation. Without any extra computational overhead and parameters
during inference, our method outperforms state-of-the-art performance. The code
is available at https://github.com/zhangchbin/RCIL.

本文研究了连续语义分割问题，提出使用表示补偿（RC）模块结构再参数化机制，以解决网络需要连续整合新类别但避免灾难性遗忘的问题。通过知识蒸馏策略，增强了模型的可塑性和稳定性。在两个有挑战性的实验场景中，无需任何额外的计算负担和参数，模型的性能超过了最先进的性能。