Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning
This addresses catastrophic forgetting for continual learning systems, offering an incremental improvement by unifying memory construction policies.
The paper tackles catastrophic forgetting in continual learning by formulating dynamic memory construction as a combinatorial optimization problem to minimize global loss across tasks, and proposes Global Pseudo-task Simulation (GPS) to approximate this online, improving accuracy on four vision benchmarks.
Continual learning faces a crucial challenge of catastrophic forgetting. To address this challenge, experience replay (ER) that maintains a tiny subset of samples from previous tasks has been commonly used. Existing ER works usually focus on refining the learning objective for each task with a static memory construction policy. In this paper, we formulate the dynamic memory construction in ER as a combinatorial optimization problem, which aims at directly minimizing the global loss across all experienced tasks. We first apply three tactics to solve the problem in the offline setting as a starting point. To provide an approximate solution to this problem in the online continual learning setting, we further propose the Global Pseudo-task Simulation (GPS), which mimics future catastrophic forgetting of the current task by permutation. Our empirical results and analyses suggest that the GPS consistently improves accuracy across four commonly used vision benchmarks. We have also shown that our GPS can serve as the unified framework for integrating various memory construction policies in existing ER works.