Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning

Continual learning (CL) remains a significant challenge for deep neural networks, as it is prone to forgetting previously acquired knowledge. Several approaches have been proposed in the literature, such as experience rehearsal, regularization, and parameter isolation, to address this problem. Although almost zero forgetting can be achieved in task-incremental learning, class-incremental learning remains highly challenging due to the problem of inter-task class separation. Limited access to previous task data makes it difficult to discriminate between classes of current and previous tasks. To address this issue, we propose'Attention-Guided Incremental Learning' (AGILE), a novel rehearsal-based CL approach that incorporates compact task attention to effectively reduce interference between tasks. AGILE utilizes lightweight, learnable task projection vectors to transform the latent representations of a shared task attention module toward task distribution. Through extensive empirical evaluation, we show that AGILE significantly improves generalization performance by mitigating task interference and outperforming rehearsal-based approaches in several CL scenarios. Furthermore, AGILE can scale well to a large number of tasks with minimal overhead while remaining well-calibrated with reduced task-recency bias. In recent years, deep neural networks (DNNs) have been shown to perform better than humans on certain specific tasks, such as Atari games (Silver et al., 2018) and classification (He et al., 2015). Although impressive, these models are trained on static data and are unable to adapt their behavior to novel tasks while maintaining performance on previous tasks when the data evolve over time (Fedus et al., 2020). Continual learning (CL) refers to a training paradigm in which DNNs are exposed to a sequence of tasks and are expected to learn potentially incrementally or online (Parisi et al., 2019). CL has remained one of the most daunting tasks for DNNs, as acquiring new information significantly deteriorates the performance of previously learned tasks, a phenomenon termed "catastrophic forgetting" (French, 1999; McCloskey & Cohen, 1989).