Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization

• URL: http://arxiv.org/abs/2310.03898v1
• Date: Thu, 5 Oct 2023 21:07:45 GMT
• Title: Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization
• Authors: Zizhao Hu, Mohammad Rostami
• Abstract summary: Generative experience replay addresses the challenge of learning new tasks accumulatively without forgetting. We introduce a time-aware regularization method to fine-tune the three training objective terms used for generative replay. Experimental results indicate that our method pushes the limit of brain-inspired continual learners under such strict settings.
• Score: 24.143811670210546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning new tasks accumulatively without forgetting remains a critical challenge in continual learning. Generative experience replay addresses this challenge by synthesizing pseudo-data points for past learned tasks and later replaying them for concurrent training along with the new tasks' data. Generative replay is the best strategy for continual learning under a strict class-incremental setting when certain constraints need to be met: (i) constant model size, (ii) no pre-training dataset, and (iii) no memory buffer for storing past tasks' data. Inspired by the biological nervous system mechanisms, we introduce a time-aware regularization method to dynamically fine-tune the three training objective terms used for generative replay: supervised learning, latent regularization, and data reconstruction. Experimental results on major benchmarks indicate that our method pushes the limit of brain-inspired continual learners under such strict settings, improves memory retention, and increases the average performance over continually arriving tasks.

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