Deep Neural Network (DNN) has achieved great success on datasets of closed
class set. However, new classes, like new categories of social media topics,
are continuously added to the real world, making it necessary to incrementally
learn. This is hard for DNN because it tends to focus on fitting to new classes
while ignoring old classes, a phenomenon known as catastrophic forgetting.
State-of-the-art methods rely on knowledge distillation and data replay
techniques but still have limitations. In this work, we analyze the causes of
catastrophic forgetting in class incremental learning, which owes to three
factors: representation drift, representation confusion, and classifier
distortion. Based on this view, we propose a two-stage learning framework with
a fixed encoder and an incrementally updated prototype classifier. The encoder
is trained with self-supervised learning to generate a feature space with high
intrinsic dimensionality, thus improving its transferability and generality.
The classifier incrementally learns new prototypes while retaining the
prototypes of previously learned data, which is crucial in preserving the
decision boundary.Our method does not rely on preserved samples of old classes,
is thus a non-exemplar based CIL method. Experiments on public datasets show
that our method can significantly outperform state-of-the-art exemplar-based
methods when they reserved 5 examplers per class, under the incremental setting
of 10 phases, by 18.24% on CIFAR-100 and 9.37% on ImageNet100.

通过分析类增量学习中灾难性遗忘的原因，我们提出了一个两阶段学习框架，其中包括一个固定编码器和一个逐步更新原型分类器。我们的方法不依赖于保留的旧类别样本，是一种非范例基于的 CIL 方法。在公共数据集上的实验证明，我们的方法在保留每类 5 个示例和 10 个阶段递增设置下，比最先进的范例基于方法在 CIFAR-100 上提高了 18.24%，在 ImageNet100 上提高了 9.37%。