Layer-Wise Feedback Signals: Dynamic Regulation for Continual Learning

ICLR 2026 Conference Submission13705 Authors

Keywords: continual learning, dynamic feedback

Abstract:

Continual learning aims to acquire new tasks while preserving performance on previously learned ones, but most methods struggle with catastrophic forgetting. Existing approaches typically treat all layers uniformly, often trading stability for plasticity or vice versa. However, different layers naturally exhibit varying levels of uncertainty (entropy) when classifying tasks. High-entropy layers tend to underfit by failing to capture task-specific patterns, while low-entropy layers risk overfitting by becoming overly confident and specialized.
To address this imbalance, we propose an entropy-aware continual learning method that employs a dynamic feedback mechanism to regulate each layer based on its entropy. Specifically, our approach reduces entropy in high-entropy layers to mitigate underfitting and increases entropy in overly confident layers to alleviate overfitting. This adaptive regulation encourages the model to converge to wider local minima, which have been shown to improve generalization. Our method is general and can be seamlessly integrated with both replay- and regularization-based approaches. Experiments on Split-CIFAR100 and Tiny-ImageNet demonstrate substantial performance gains over state-of-the-art baselines.

Primary Area: transfer learning, meta learning, and lifelong learning

Submission Number: 13705