Unlike primates, training artificial neural networks on changing data
distributions leads to a rapid decrease in performance on old tasks. This
phenomenon is commonly referred to as catastrophic forgetting. In this paper,
we investigate the representational changes that underlie this performance
decrease and identify three distinct processes that together account for the
phenomenon. The largest component is a misalignment between hidden
representations and readout layers. Misalignment occurs due to learning on
additional tasks and causes internal representations to shift. Representational
geometry is partially conserved under this misalignment and only a small part
of the information is irrecoverably lost. All types of representational changes
scale with the dimensionality of hidden representations. These insights have
implications for deep learning applications that need to be continuously
updated, but may also aid aligning ANN models to the rather robust biological
vision.

在这篇论文中，我们研究了导致人工神经网络在变化的数据分布上训练后旧任务性能迅速下降的表征性变化，并确定了解释这一现象的三个不同过程。最主要的成分是隐藏表征与输出层之间的不对齐，这种不对齐是由于在其他任务上的学习造成的，它导致内部表征发生位移。表征几何在这种不对齐下部分保留，并且只有一小部分信息是无法恢复的。所有类型的表征性变化与隐藏表征的维度成比例。这些洞见对需要不断更新的深度学习应用具有重要的意义，同时也对将人工神经网络模型与相对稳健的生物视觉对齐有帮助。