AracNet: Revealing Debiasing Signals across Layers with Shallow Monitors

Deep neural networks often show poor generalization whentrained on biased datasets presenting spurious relations between sam-ples and target labels. Unsupervised debiasing methods aim at mitigat-ing models’ dependency on bias, without relying on bias information.Bias shortcuts are typically learned early in training, with the modelrapidly overfitting the few samples not sharing the same related at-tributes. Avoiding bias conflicting memorization for mining bias infor-mation useful for mitigation (e.g., in the form of pseudo-labels) is still afundamental open issue in this context, with existing approaches propos-ing to learn for a few epochs or to rely on unbiased validation sets for earlystopping. In this work, we propose to exploit intermediate layers’ featuresfor achieving bias mitigation robust to the memorization problem, replac-ing the when to stop with a where to look paradigm. Specifically, we pro-pose AracNet, an unsupervised debiasing framework for self-mitigation.Attaching one linear layer to each network’s block, which we refer to asShallow Monitor, we obtain a useful debiasing signal capable of miti-gating bias dependency in the same model when randomly reinitialized.Our results on typical benchmark datasets with single and multiple biasesconfirm the effectiveness of the proposed approach, paving the way forfurther research in understanding how bias propagates in trained models.Code is available at https://github.com/Malga-Vision/AracNet