Label noise is a pervasive problem in deep learning that often compromises
the generalization performance of trained models. Recently, leveraging
privileged information (PI) -- information available only during training but
not at test time -- has emerged as an effective approach to mitigate this
issue. Yet, existing PI-based methods have failed to consistently outperform
their no-PI counterparts in terms of preventing overfitting to label noise. To
address this deficiency, we introduce Pi-DUAL, an architecture designed to
harness PI to distinguish clean from wrong labels. Pi-DUAL decomposes the
output logits into a prediction term, based on conventional input features, and
a noise-fitting term influenced solely by PI. A gating mechanism steered by PI
adaptively shifts focus between these terms, allowing the model to implicitly
separate the learning paths of clean and wrong labels. Empirically, Pi-DUAL
achieves significant performance improvements on key PI benchmarks (e.g., +6.8%
on ImageNet-PI), establishing a new state-of-the-art test set accuracy.
Additionally, Pi-DUAL is a potent method for identifying noisy samples
post-training, outperforming other strong methods at this task. Overall,
Pi-DUAL is a simple, scalable and practical approach for mitigating the effects
of label noise in a variety of real-world scenarios with PI.

Pi-DUAL 是一种利用特权信息来减轻标签噪声的简单、可扩展和实用的方法，通过将输出的逻辑分解为基于传统输入特征的预测项和仅受特权信息影响的噪声适配项，Pi-DUAL 能够自适应地在这两个项之间切换焦点，从而使模型隐性地区分干净标签和错误标签的学习路径，实现了在关键特权信息基准上显著提高性能（例如，在 ImageNet-PI 上提高了 6.8% 的测试集准确率），此外，Pi-DUAL 也是一种在训练后识别嘈杂样本的有效方法，在这方面超过了其他强大的方法。