We present a new replay-based method of continual classification learning that we term "conditional replay" which generates samples and labels together by sampling from a distribution conditioned on the class. We compare conditional replay to another replay-based continual learning paradigm (which we term "marginal replay") that generates samples independently of their class and assigns labels in a separate step. The main improvement in conditional replay is that labels for generated samples need not be inferred, which reduces the margin for error in complex continual classification learning tasks. We demonstrate the effectiveness of this approach using novel and standard benchmarks constructed from MNIST and FashionMNIST data, and compare to the regularization-based EWC method.

本文介绍了一种基于回放的持续分类学习方法，称为“条件重放”，该方法通过从以类为条件的分布中进行采样生成样本和标签。作者将条件重放与另一种基于回放的持续学习范式（称为“边际重放”）进行了比较，并在新的和标准的MNIST和FashionMNIST数据集构建的基准测试中展示了这种方法的有效性，并与基于正则化的“弹性权重合并”（EWC）方法进行比较。

边缘重放 vs 条件重放：面向连续学习