Recent studies have noted an intriguing phenomenon termed Neural Collapse,
that is, when the neural networks establish the right correlation between
feature spaces and the training targets, their last-layer features, together
with the classifier weights, will collapse into a stable and symmetric
structure. In this paper, we extend the investigation of Neural Collapse to the
biased datasets with imbalanced attributes. We observe that models will easily
fall into the pitfall of shortcut learning and form a biased, non-collapsed
feature space at the early period of training, which is hard to reverse and
limits the generalization capability. To tackle the root cause of biased
classification, we follow the recent inspiration of prime training, and propose
an avoid-shortcut learning framework without additional training complexity.
With well-designed shortcut primes based on Neural Collapse structure, the
models are encouraged to skip the pursuit of simple shortcuts and naturally
capture the intrinsic correlations. Experimental results demonstrate that our
method induces better convergence properties during training, and achieves
state-of-the-art generalization performance on both synthetic and real-world
biased datasets.

本文探讨了神经塌缩（Neural Collapse）现象在不平衡属性的偏倚数据集中的影响，提出了一种避免捷径学习的框架，通过设计神经塌缩结构的快捷方式引导模型，以捕捉内在的相关性，从而解决了偏倚分类的根本原因，实验证明该方法在训练过程中具有更好的收敛性能并在综合和真实世界的偏倚数据集上实现了最新的泛化性能。