Automating Continual Learning

• URL: http://arxiv.org/abs/2312.00276v2
• Date: Sat, 30 Mar 2024 03:25:51 GMT
• Title: Automating Continual Learning
• Authors: Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber,
• Abstract summary: General-purpose learning systems should improve themselves in open-ended fashion in ever-changing environments. We propose Automated Continual Learning (ACL) to train self-referential neural networks to meta-learn their own in-context continual (meta-)learning algorithms.
• Score: 42.710124929514066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: General-purpose learning systems should improve themselves in open-ended fashion in ever-changing environments. Conventional learning algorithms for neural networks, however, suffer from catastrophic forgetting (CF) -- previously acquired skills are forgotten when a new task is learned. Instead of hand-crafting new algorithms for avoiding CF, we propose Automated Continual Learning (ACL) to train self-referential neural networks to meta-learn their own in-context continual (meta-)learning algorithms. ACL encodes all desiderata -- good performance on both old and new tasks -- into its meta-learning objectives. Our experiments demonstrate that ACL effectively solves "in-context catastrophic forgetting"; our ACL-learned algorithms outperform hand-crafted ones, e.g., on the Split-MNIST benchmark in the replay-free setting, and enables continual learning of diverse tasks consisting of multiple few-shot and standard image classification datasets.

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