Continual Learning (CL) is crucial for enabling networks to dynamically adapt as they learn new tasks sequentially, accommodating new data and classes without catastrophic forgetting. Diverging from conventional perspectives on CL, our paper introduces a new perspective wherein forgetting could actually benefit the sequential learning paradigm. Specifically, we present BiasPruner, a CL framework that intentionally forgets spurious correlations in the training data that could lead to shortcut learning. Utilizing a new bias score that measures the contribution of each unit in the network to learning spurious features, BiasPruner prunes those units with the highest bias scores to form a debiased subnetwork preserved for a given task. As BiasPruner learns a new task, it constructs a new debiased subnetwork, potentially incorporating units from previous subnetworks, which improves adaptation and performance on the new task. During inference, BiasPruner employs a simple task-agnostic approach to select the best debiased subnetwork for predictions. We conduct experiments on three medical datasets for skin lesion classification and chest X-Ray classification and demonstrate that BiasPruner consistently outperforms SOTA CL methods in terms of classification performance and fairness. Our code is available here.

翻译：持续学习（Continual Learning, CL）对于使网络能够按顺序学习新任务、适应新数据和类别而避免灾难性遗忘至关重要。与传统持续学习视角不同，本文提出一种新观点：遗忘实际上可能有益于序列学习范式。具体而言，我们提出BiasPruner——一种通过主动遗忘训练数据中可能导致捷径学习的伪相关性来实现持续学习的框架。该方法利用一种新的偏置评分来度量网络中每个单元对学习伪特征的贡献度，进而剪除偏置评分最高的单元，形成针对特定任务保留的去偏子网络。当BiasPruner学习新任务时，它会构建新的去偏子网络（可能整合先前子网络的单元），从而提升对新任务的适应能力与性能。在推理阶段，BiasPruner采用简单的任务无关方法选择最佳去偏子网络进行预测。我们在皮肤病变分类和胸部X光分类的三个医学数据集上开展实验，结果表明BiasPruner在分类性能与公平性方面均持续优于当前最先进的持续学习方法。我们的代码已开源。