CLASS-INCREMENTAL LEARNING USING GENERATIVE EXPERIENCE REPLAY BASED ON TIME-AWARE REGULARIZATION
Primary Area: transfer learning, meta learning, and lifelong learning
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Keywords: lifelong learning, continual learning, class-incremental learning, regularization
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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 a brain-inspired continual learner under such strict settings, improves memory retention, and increases the average performance over continually arriving tasks.
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Submission Number: 9137
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