Class-Incremental Learning updates a deep classifier with new categories while maintaining the previously observed class accuracy. Regularizing the neural network weights is a common method to prevent forgetting previously learned classes while learning novel ones. However, existing regularizers use a constant magnitude throughout the learning sessions, which may not reflect the varying levels of difficulty of the tasks encountered during incremental learning. This study investigates the necessity of adaptive regularization in Class-Incremental Learning, which dynamically adjusts the regularization strength according to the complexity of the task at hand. We propose a Bayesian Optimization-based approach to automatically determine the optimal regularization magnitude for each learning task. Our experiments on two datasets via two regularizers demonstrate the importance of adaptive regularization for achieving accurate and less forgetful visual incremental learning.

翻译：类增量学习在更新深度分类器以纳入新类别的同时，需保持对先前观察类别的准确率。对神经网络权重进行正则化是防止在学习新类别时遗忘已学类别的常用方法。然而，现有正则化器在整个学习过程中使用恒定幅度，这无法反映增量学习过程中所遇任务难度变化。本研究探讨了类增量学习中自适应正则化的必要性——即根据当前任务的复杂度动态调整正则化强度。我们提出一种基于贝叶斯优化的方法，可自动确定每个学习任务的最优正则化幅度。在两种数据集上通过两种正则化器进行的实验表明，自适应正则化对于实现高精度且低遗忘的可视化增量学习至关重要。