Adaptive Shortcut Debiasing for Online Continual Learning
Doyoung Kim Dongmin Park Yooju Shin Jihwan Bang Hwanjun Song Jae-Gil Lee
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%.