However, they often suffer from costly training and inference due to a tremendous amount of computations through neural networks.

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.

We study envy-free policy teaching.

Our experimental results have also shown that the SNNs thus trained can achieve comparable accuracies to their teacher ANNs on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets.