Selecting Related Knowledge via Efficient Channel Attention for Online Continual Learning

TL;DR

This paper introduces SRKOCL, a framework for online continual learning that uses efficient channel attention to select relevant knowledge for each task, improving performance and reducing forgetting.

Contribution

The paper proposes a novel framework that incorporates channel attention to select related knowledge, enhancing online continual learning effectiveness.

Findings

SRKOCL outperforms state-of-the-art methods on multiple benchmarks.

The attention mechanism effectively identifies task-specific knowledge.

Combining experience replay and knowledge distillation mitigates catastrophic forgetting.

Abstract

Continual learning aims to learn a sequence of tasks by leveraging the knowledge acquired in the past in an online-learning manner while being able to perform well on all previous tasks, this ability is crucial to the artificial intelligence (AI) system, hence continual learning is more suitable for most real-word and complex applicative scenarios compared to the traditional learning pattern. However, the current models usually learn a generic representation base on the class label on each task and an effective strategy is selected to avoid catastrophic forgetting. We postulate that selecting the related and useful parts only from the knowledge obtained to perform each task is more effective than utilizing the whole knowledge. Based on this fact, in this paper we propose a new framework, named Selecting Related Knowledge for Online Continual Learning (SRKOCL), which incorporates an additional efficient channel attention mechanism to pick the particular related knowledge for every task. Our model also combines experience replay and knowledge distillation to circumvent the catastrophic forgetting. Finally, extensive experiments are conducted on different benchmarks and the competitive experimental results demonstrate that our proposed SRKOCL is a promised approach against the state-of-the-art.