Issues for Continual Learning in the Presence of Dataset BiasDownload PDF

Published: 11 Jan 2023, Last Modified: 05 May 2023AAAI23 Bridge Continual CausalityReaders: Everyone
Keywords: Continual learning, Bias, Causality
Abstract: While most continual learning algorithms have focused on tackling the stability-plasticity dilemma, they have overlooked the effects of the knowledge transfer when the dataset is biased — namely, when some unintended spurious correlations, not the true causal structures, of the tasks are learned from the biased dataset. In that case, how would they affect learning future tasks or the knowledge already learned from the past tasks? In this work, we design systematic experiments with a synthetic biased dataset and try to answer the above question from our empirical findings. Namely, we first show that standard continual learning methods that are unaware of dataset bias can transfer biases from one task to another, both forward and backward. In addition, we find that naively using existing debiasing methods after each continual learning step can lead to significant forgetting of past tasks and reduced overall continual learning performance. These findings highlight the need for a causality-aware design of continual learning algorithms to prevent both bias transfers and catastrophic forgetting.
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