Efficient Lifelong Learning with A-GEM

Understand

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.