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 refers to the ability to continually learn over time by accommodating new knowledge while retaining previously learned experience. While this concept is inherent in human learning%2C current machine learning methods are highly prone to overwrite previously learned patterns and thus forget past experience. Instead%2C model parameters should be updated selectively and carefully%2C avoiding unnecessary forgetting while optimally leveraging previously learned patterns to accelerate future learning. Since hand-crafting effective update mechanisms is difficult%2C we propose meta-learning a transformer-based optimizer to enhance CL. This meta-learned optimizer uses attention to learn the complex relationships between model parameters across a stream of tasks%2C and is designed to generate effective weight updates for the current task while preventing catastrophic forgetting on previously encountered tasks. Evaluations on benchmark datasets like SplitMNIST%2C RotatedMNIST%2C and SplitCIFAR-100 affirm the efficacy of the proposed approach in terms of both forward and backward transfer%2C even on small sets of labeled data%2C highlighting the advantages of integrating a meta-learned optimizer within the continual learning framework.?quality=80&w=800)
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