  Lifelong learning remains an open problem. One of its main difficulties is
catastrophic forgetting. Many dynamic expansion approaches have been proposed
to address this problem, but they all use homogeneous models of predefined
structure for all tasks. The common original model and expansion structures
ignore the requirement of different model structures on different tasks, which
leads to a less compact model for multiple tasks and causes the model size to
increase rapidly as the number of tasks increases. Moreover, they can not
perform best on all tasks. To solve those problems, in this paper, we propose a
new lifelong learning framework named Searchable Extension Units (SEU) by
introducing Neural Architecture Search into lifelong learning, which breaks
down the need for a predefined original model and searches for specific
extension units for different tasks, without compromising the performance of
the model on different tasks. Our approach can obtain a much more compact model
without catastrophic forgetting. The experimental results on the PMNIST, the
split CIFAR10 dataset, the split CIFAR100 dataset, and the Mixture dataset
empirically prove that our method can achieve higher accuracy with much smaller
model, whose size is about 25-33 percentage of that of the state-of-the-art
methods.
