Representation Ensembling for Synergistic Lifelong Learning with Quasilinear Complexity

Abstract

In biological learning, data are used to improve performance not only on the current task, but also on previously encountered, and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called catastrophic forgetting). Many recent approaches for continual or lifelong learning have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on both past tasks (backward transfer) and future tasks (forward transfer) with any new data. Our key insight is that even though learners trained on other tasks often cannot make useful decisions on the current task (the two tasks may have non-overlapping classes, for example), they may have learned representations that are useful for this task. Thus, although ensembling decisions is not possible, ensembling representations can be beneficial whenever the distributions across tasks are sufficiently similar. Moreover, we can ensemble representations learned independently across tasks in quasilinear space and time. We therefore propose two algorithms: representation ensembles of (1) trees and (2) networks. Both algorithms demonstrate both forward and backward transfer in a variety of simulated and real data scenarios, including tabular, image, and spoken, and adversarial tasks. This is in stark contrast to the reference algorithms we compared to, all of which failed to transfer either forward or backward, or both, despite that many of them require quadratic space or time complexity.