  In the image classification task, deep neural networks frequently rely on
bias attributes that are spuriously correlated with a target class in the
presence of dataset bias, resulting in degraded performance when applied to
data without bias attributes. The task of debiasing aims to compel classifiers
to learn intrinsic attributes that inherently define a target class rather than
focusing on bias attributes. While recent approaches mainly focus on
emphasizing the learning of data samples without bias attributes (i.e.,
bias-conflicting samples) compared to samples with bias attributes (i.e.,
bias-aligned samples), they fall short of directly guiding models where to
focus for learning intrinsic features. To address this limitation, this paper
proposes a method that provides the model with explicit spatial guidance that
indicates the region of intrinsic features. We first identify the intrinsic
features by investigating the class-discerning common features between a
bias-aligned (BA) sample and a bias-conflicting (BC) sample (i.e.,
bias-contrastive pair). Next, we enhance the intrinsic features in the BA
sample that are relatively under-exploited for prediction compared to the BC
sample. To construct the bias-contrastive pair without using bias information,
we introduce a bias-negative score that distinguishes BC samples from BA
samples employing a biased model. The experiments demonstrate that our method
achieves state-of-the-art performance on synthetic and real-world datasets with
various levels of bias severity.
