Exemplar-free class-incremental learning (CIL) poses several challenges since
it prohibits the rehearsal of data from previous tasks and thus suffers from
catastrophic forgetting. Recent approaches to incrementally learning the
classifier by freezing the feature extractor after the first task have gained
much attention. In this paper, we explore prototypical networks for CIL, which
generate new class prototypes using the frozen feature extractor and classify
the features based on the Euclidean distance to the prototypes. In an analysis
of the feature distributions of classes, we show that classification based on
Euclidean metrics is successful for jointly trained features. However, when
learning from non-stationary data, we observe that the Euclidean metric is
suboptimal and that feature distributions are heterogeneous. To address this
challenge, we revisit the anisotropic Mahalanobis distance for CIL. In
addition, we empirically show that modeling the feature covariance relations is
better than previous attempts at sampling features from normal distributions
and training a linear classifier. Unlike existing methods, our approach
generalizes to both many- and few-shot CIL settings, as well as to
domain-incremental settings. Interestingly, without updating the backbone
network, our method obtains state-of-the-art results on several standard
continual learning benchmarks. Code is available at
this https URL

无先例的课程增量学习中，使用冻结的特征提取器基于欧氏距离生成类原型并进行分类，然而，欧氏距离在非静态数据学习时表现亚优，特征分布异质。为了解决这个挑战，我们重新考虑适用于课程增量学习的各向异性马氏距离，并经验证明建模特征协方差关系要优于从正态分布中采样特征和训练线性分类器的先前方法，且我们的方法在多样本和少样本课程增量学习设置以及域增量学习设置上具有广泛适用性。有趣的是，在不更新主干网络的情况下，我们的方法在多个标准连续学习基准上取得了最先进的结果。