Continual learning refers to the ability to acquire and transfer knowledge
without catastrophically forgetting what was previously learned. In this work,
we consider \emph{few-shot} continual learning in classification tasks, and we
propose a novel method, Variational Prototype Replays, that efficiently
consolidates and recalls previous knowledge to avoid catastrophic forgetting.
In each classification task, our method learns a set of variational prototypes
with their means and variances, where embedding of the samples from the same
class can be represented in a prototypical distribution and
class-representative prototypes are separated apart. To alleviate catastrophic
forgetting, our method replays one sample per class from previous tasks, and
correspondingly matches newly predicted embeddings to their nearest
class-representative prototypes stored from previous tasks. Compared with
recent continual learning approaches, our method can readily adapt to new tasks
with more classes without requiring the addition of new units. Furthermore, our
method is more memory efficient since only class-representative prototypes with
their means and variances, as well as only one sample per class from previous
tasks need to be stored. Without tampering with the performance on initial
tasks, our method learns novel concepts given a few training examples of each
class in new tasks.

本研究提出一种新的方法 —— 变分原型重放，以实现分类任务中的少样本连续学习，避免了灾难性遗忘。该方法通过学习一组可变原型来表示属于同一类的样本嵌入，并将类代表原型分别分开。此外，该方法还通过从以前的任务中重放一个样本来避免灾难性遗忘。相较于最近的连续学习方法，该方法能够在不需要添加新单元的情况下轻松适应更多类的新任务，并且更具有内存效率。