Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a
sequence of tasks without having access to previous task data. In this paper,
we consider the challenging Cold Start scenario in which insufficient data is
available in the first task to learn a high-quality backbone. This is
especially challenging for EFCIL since it requires high plasticity, which
results in feature drift which is difficult to compensate for in the
exemplar-free setting. To address this problem, we propose a simple and
effective approach that consolidates feature representations by regularizing
drift in directions highly relevant to previous tasks and employs prototypes to
reduce task-recency bias. Our method, called Elastic Feature Consolidation
(EFC), exploits a tractable second-order approximation of feature drift based
on an Empirical Feature Matrix (EFM). The EFM induces a pseudo-metric in
feature space which we use to regularize feature drift in important directions
and to update Gaussian prototypes used in a novel asymmetric cross entropy loss
which effectively balances prototype rehearsal with data from new tasks.
Experimental results on CIFAR-100, Tiny-ImageNet, ImageNet-Subset and
ImageNet-1K demonstrate that Elastic Feature Consolidation is better able to
learn new tasks by maintaining model plasticity and significantly outperform
the state-of-the-art.

Elastic Feature Consolidation (EFC) 通过对于 feature drift 的正则化，以及使用高度相关的方向和高斯原型来更新重要方向的特征漂移，成功解决了 Exemplar-Free Class Incremental Learning (EFCIL) 在 Cold Start 情景下的挑战，并在学习新任务时保持了模型的可塑性，明显优于最先进方法。