Optimal Task Order for Continual Learning of Multiple Tasks

• URL: http://arxiv.org/abs/2502.03350v1
• Date: Wed, 05 Feb 2025 16:43:58 GMT
• Title: Optimal Task Order for Continual Learning of Multiple Tasks
• Authors: Ziyan Li, Naoki Hiratani,
• Abstract summary: Continual learning of multiple tasks remains a major challenge for neural networks. Here, we investigate how task order influences continual learning and propose a strategy for optimizing it. Our work thus presents a generalizable framework for task-order optimization in task-incremental continual learning.
• Score: 3.591122855617648
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• Abstract: Continual learning of multiple tasks remains a major challenge for neural networks. Here, we investigate how task order influences continual learning and propose a strategy for optimizing it. Leveraging a linear teacher-student model with latent factors, we derive an analytical expression relating task similarity and ordering to learning performance. Our analysis reveals two principles that hold under a wide parameter range: (1) tasks should be arranged from the least representative to the most typical, and (2) adjacent tasks should be dissimilar. We validate these rules on both synthetic data and real-world image classification datasets (Fashion-MNIST, CIFAR-10, CIFAR-100), demonstrating consistent performance improvements in both multilayer perceptrons and convolutional neural networks. Our work thus presents a generalizable framework for task-order optimization in task-incremental continual learning.

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