  There is an increasing need of continual learning in dynamic systems, such as
the self-driving vehicle, the surveillance drone, and the robotic system. Such
a system requires learning from the data stream, training the model to preserve
previous information and adapt to a new task, and generating a single-headed
vector for future inference. Different from previous approaches with dynamic
structures, this work focuses on a single network and model segmentation to
prevent catastrophic forgetting. Leveraging the redundant capacity of a single
network, model parameters for each task are separated into two groups: one
important group which is frozen to preserve current knowledge, and secondary
group to be saved (not pruned) for a future learning. A fixed-size memory
containing a small amount of previously seen data is further adopted to assist
the training. Without additional regularization, the simple yet effective
approach of PST successfully incorporates multiple tasks and achieves the
state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and
CIFAR-100 datasets. Moreover, the segmented training significantly improves
computation efficiency in continual learning.
