Continual Learning of Achieving Forgetting-free and Positive Knowledge Transfer

TL;DR

This paper introduces ETCL, a continual learning method that prevents forgetting and promotes positive knowledge transfer between tasks by modeling CL as an optimization problem and using task-specific masks and gradient alignment.

Contribution

The paper proposes a novel ETCL approach that ensures forgetting-free learning with positive forward and backward knowledge transfer, including theoretical bounds and online task similarity detection.

Findings

ETCL outperforms strong baselines on various task sequences.

It effectively prevents catastrophic forgetting while enabling positive knowledge transfer.

Theoretical bounds guide the design of the transfer strategies.

Abstract

Existing research on continual learning (CL) of a sequence of tasks focuses mainly on dealing with catastrophic forgetting (CF) to balance the learning plasticity of new tasks and the memory stability of old tasks. However, an ideal CL agent should not only be able to overcome CF, but also encourage positive forward and backward knowledge transfer (KT), i.e., using the learned knowledge from previous tasks for the new task learning (namely FKT), and improving the previous tasks' performance with the knowledge of the new task (namely BKT). To this end, this paper first models CL as an optimization problem in which each sequential learning task aims to achieve its optimal performance under the constraint that both FKT and BKT should be positive. It then proposes a novel Enhanced Task Continual Learning (ETCL) method, which achieves forgetting-free and positive KT. Furthermore, the bounds that can lead to negative FKT and BKT are estimated theoretically. Based on the bounds, a new strategy for online task similarity detection is also proposed to facilitate positive KT. To overcome CF, ETCL learns a set of task-specific binary masks to isolate a sparse sub-network for each task while preserving the performance of a dense network for the task. At the beginning of a new task learning, ETCL tries to align the new task's gradient with that of the sub-network of the previous most similar task to ensure positive FKT. By using a new bi-objective optimization strategy and an orthogonal gradient projection method, ETCL updates only the weights of previous similar tasks at the classification layer to achieve positive BKT. Extensive evaluations demonstrate that the proposed ETCL markedly outperforms strong baselines on dissimilar, similar, and mixed task sequences.