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Continual learning with a predictive coding based classifier
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DOI：10.1016/j.asoc.2025.114265.png)
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Abstract
En 中文
• A novel computationally efficient Continual Learning (CL) method, termed Continual Learning with a Predictive Coding based Classifier (CLPC2), which enables incremental learning of new tasks using a generative replay strategy while supporting parallel learning. • The application of the proposed algorithm to two different architectures, namely Fully Connected Networks (CLPC2-FCN) and Convolutional Neural Networks (CLPC2-CNN). • A comprehensive evaluation and comparison of CLPC2’s performance with existing CL methods on the MNIST, CIFAR-10, and CIFAR-100 datasets in three different CL scenarios. The results show that CLPC2 achieves higher average classification accuracy in the challenging scenarios of Class-IL and Domain-IL on MNIST and CIFAR-10, while also offering benefits such as in-parallel learning.
Keywords:
Predictive coding
Continual learning
Replay
Generative model
Local learning
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