Navigate Beyond Shortcuts: Debiased Learning through the Lens of Neural Collapse

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 sym-metric structure. In this paper, we extend the investigation of Neural Collapse to the biased datasets with im-balanced 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 ad-ditional training complexity. With well-designed shortcut primes based on Neural Collapse structure, the models are encouraged to skip the pursuit of simple shortcuts and nat-urally capture the intrinsic correlations. Experimental re-sults demonstrate that our method induces better conver-gence properties during training, and achieves state-of-the-art generalization performance on both synthetic and real-world biased datasets.