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

• URL: http://arxiv.org/abs/2405.13978v1
• Date: Wed, 22 May 2024 20:29:15 GMT
• Title: Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning
• Authors: Prashant Bhat, Bharath Renjith, Elahe Arani, Bahram Zonooz,
• Abstract summary: Attention-Guided Incremental Learning' (AGILE) is a rehearsal-based CL approach that incorporates compact task attention to effectively reduce interference between tasks. AGILE significantly improves generalization performance by mitigating task interference and outperforming rehearsal-based approaches in several CL scenarios.
• Score: 17.236861687708096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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