  In replay-based methods for continual learning, replaying input samples in
episodic memory has shown its effectiveness in alleviating catastrophic
forgetting. However, the potential key factor of cross-entropy loss with
softmax in causing catastrophic forgetting has been underexplored. In this
paper, we analyze the effect of softmax and revisit softmax masking with
negative infinity to shed light on its ability to mitigate catastrophic
forgetting. Based on the analyses, it is found that negative infinity masked
softmax is not always compatible with dark knowledge. To improve the
compatibility, we propose a general masked softmax that controls the stability
by adjusting the gradient scale to old and new classes. We demonstrate that
utilizing our method on other replay-based methods results in better
performance, primarily by enhancing model stability in continual learning
benchmarks, even when the buffer size is set to an extremely small value.
