Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks. In this work, we investigate SB in long-tailed image recognition and find the tail classes suffer more severely from SB, which harms the generalization performance of such underrepresented classes. We empirically report that self-supervised learning (SSL) can mitigate SB and perform in complementary to the supervised counterpart by enriching the features extracted from tail samples and consequently taking better advantage of such rare samples. However, standard SSL methods are designed without explicitly considering the inherent data distribution in terms of classes and may not be optimal for long-tailed distributed data. To address this limitation, we propose a novel SSL method tailored to imbalanced data. It leverages SSL by triple diverse levels, i.e., holistic-, partial-, and augmented-level, to enhance the learning of predictive complex patterns, which provides the potential to overcome the severe SB on tail data. Both quantitative and qualitative experimental results on five long-tailed benchmark datasets show our method can effectively mitigate SB and significantly outperform the competing state-of-the-arts.

翻译：简单性偏差（Simplicity Bias, SB）是指深度神经网络在监督判别任务中倾向于依赖更简单的预测模式，而忽略某些复杂特征的现象。本文研究了长尾图像识别中的SB现象，发现尾部类别受SB影响更为严重，损害了这些欠代表性类别的泛化性能。我们通过实验证明，自监督学习（Self-Supervised Learning, SSL）能够缓解SB，并通过丰富尾部样本提取的特征来更好地利用这些稀有样本，从而与监督学习形成互补。然而，标准的SSL方法在设计时未明确考虑类别层面的固有数据分布，可能不适用于长尾分布数据。为解决此限制，我们提出了一种针对不平衡数据的新型SSL方法。该方法通过三重不同层次（即整体层次、局部层次和增强层次）利用SSL，增强复杂预测模式的学习，从而提供克服尾部数据上严重SB的潜力。在五个长尾基准数据集上的定量和定性实验结果表明，我们的方法能有效缓解SB，并显著优于当前最先进的竞争方法。