  In lifelong learning, the learner is presented with a sequence of tasks,
incrementally building a data-driven prior which may be leveraged to speed up
learning of a new task. In this work, we investigate the efficiency of current
lifelong approaches, in terms of sample complexity, computational and memory
cost. Towards this end, we first introduce a new and a more realistic
evaluation protocol, whereby learners observe each example only once and
hyper-parameter selection is done on a small and disjoint set of tasks, which
is not used for the actual learning experience and evaluation. Second, we
introduce a new metric measuring how quickly a learner acquires a new skill.
Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017),
dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance
as GEM, while being almost as computationally and memory efficient as EWC
(Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we
show that all algorithms including A-GEM can learn even more quickly if they
are provided with task descriptors specifying the classification tasks under
consideration. Our experiments on several standard lifelong learning benchmarks
demonstrate that A-GEM has the best trade-off between accuracy and efficiency.
