Task Ordering Matters for Incremental Learning
Zhaonan Yang, Huiyun Li · 2021
Current incremental learning is confronted with the problem of catastrophic forgetting. Existing work usually addressed this problem by enlarging the sample database. But few people pay attention to the order-sensitive problem in incremental learning. In this article, we propose a task sorting method with the feature similarity between the consecutive tasks. Experimental results on CIFAR-100 and CORe50 datasets demonstrate that the learning sorting matters for incremental learning. The task sorted with the highest feature similarity will get a better performance than that of random sorting.