  Plasticity-stability dilemma is a main problem for incremental learning,
where plasticity is referring to the ability to learn new knowledge, and
stability retains the knowledge of previous tasks. Many methods tackle this
problem by storing previous samples, while in some applications, training data
from previous tasks cannot be legally stored. In this work, we propose to
employ mode connectivity in loss landscapes to achieve better
plasticity-stability trade-off without any previous samples. We give an
analysis of why and how to connect two independently optimized optima of
networks, null-space projection for previous tasks and simple SGD for the
current task, can attain a meaningful balance between preserving already
learned knowledge and granting sufficient flexibility for learning a new task.
This analysis of mode connectivity also provides us a new perspective and
technology to control the trade-off between plasticity and stability. We
evaluate the proposed method on several benchmark datasets. The results
indicate our simple method can achieve notable improvement, and perform well on
both the past and current tasks. On 10-split-CIFAR-100 task, our method
achieves 79.79% accuracy, which is 6.02% higher. Our method also achieves 6.33%
higher accuracy on TinyImageNet. Code is available at
https://github.com/lingl1024/Connector.
