In the field of autonomous driving, even a meticulously trained model can encounter failures when faced with unfamiliar sceanrios. One of these scenarios can be formulated as an online continual learning (OCL) problem. That is, data come in an online fashion, and models are updated according to these streaming data. Two major OCL challenges are catastrophic forgetting and data imbalance. To address these challenges, in this paper, we propose an Analytic Exemplar-Free Online Continual Learning (AEF-OCL). The AEF-OCL leverages analytic continual learning principles and employs ridge regression as a classifier for features extracted by a large backbone network. It solves the OCL problem by recursively calculating the analytical solution, ensuring an equalization between the continual learning and its joint-learning counterpart, and works without the need to save any used samples (i.e., exemplar-free). Additionally, we introduce a Pseudo-Features Generator (PFG) module that recursively estimates the deviation of real features. The PFG generates offset pseudo-features following a normal distribution, thereby addressing the data imbalance issue. Experimental results demonstrate that despite being an exemplar-free strategy, our method outperforms various methods on the autonomous driving SODA10M dataset. Source code is available at https://github.com/ZHUANGHP/Analytic-continual-learning.

翻译：在自动驾驶领域，即使经过精心训练的模型在面对陌生场景时也可能出现故障。此类场景可被建模为在线持续学习问题，即数据以在线方式到达，模型根据这些流式数据进行更新。在线持续学习面临两大挑战：灾难性遗忘与数据不平衡。为应对这些挑战，本文提出一种分析式无样本在线持续学习（AEF-OCL）方法。该方法基于分析式持续学习原理，采用岭回归作为大型骨干网络所提取特征的分类器。通过递归计算解析解，AEF-OCL实现了持续学习与其联合学习对应形式之间的均衡，且无需保存任何已使用样本（即无样本）。此外，我们引入伪特征生成器（PFG）模块，该模块递归估计真实特征的偏差，并生成符合正态分布的偏移伪特征，从而解决数据不平衡问题。实验结果表明，尽管采用无样本策略，我们的方法在自动驾驶SODA10M数据集上仍优于多种现有方法。源代码发布于 https://github.com/ZHUANGHP/Analytic-continual-learning。