We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment. By the observed high-attention property of the shortcut bias, highly-activated features are considered candidates for debiasing. More importantly, resolving the limitation of the online environment where prior knowledge and auxiliary data are not ready, two novel techniques -- feature map fusion and adaptive intensity shifting -- enable us to automatically determine the appropriate level and proportion of the candidate shortcut features to be dropped. Extensive experiments on five benchmark datasets demonstrate that, when combined with various OCL algorithms, DropTop increases the average accuracy by up to 10.4% and decreases the forgetting by up to 63.2%.

翻译：我们提出一种新颖框架DropTop，用于抑制在线持续学习（OCL）中的快捷方式偏差，同时能自适应因环境持续变化而导致的偏差程度差异。基于所观测到的快捷方式偏差的高注意力特性，高激活特征被视为去偏的候选对象。更重要的是，针对在线环境中缺乏先验知识与辅助数据的局限性，两项创新技术——特征图融合与自适应强度偏移——使我们能够自动确定候选快捷方式特征的适当丢弃比例与强度。在五个基准数据集上的广泛实验表明，当与多种OCL算法结合时，DropTop可将平均准确率提升高达10.4%，并将遗忘率降低高达63.2%。