Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a priori. Our proposed method can not only successfully learn the clean target distribution from a dirty dataset but also can estimate the underlying noise pattern. To this end, we leverage a mixture-of-experts model that can distinguish two different types of predictive uncertainty, aleatoric and epistemic uncertainty. We show that the ability to estimate the uncertainty plays a significant role in elucidating the corruption patterns as these two objectives are tightly intertwined. We also present a novel validation scheme for evaluating the performance of the corruption pattern estimation. Our proposed method is extensively assessed in terms of both robustness and corruption pattern estimation through a number of domains, including computer vision and natural language processing.

翻译：鲁棒学习方法旨在从含有噪声和损坏的训练数据中学习干净的目标分布，这类方法通常预先假设特定的损坏模式。我们提出的方法不仅能成功地从脏数据集中学习干净的目标分布，还能估计潜在的噪声模式。为此，我们利用了一种混合专家模型，该模型能够区分两种不同类型的预测不确定性——偶然不确定性和认知不确定性。我们表明，由于这两个目标紧密交织，估计不确定性的能力在阐明损坏模式中起着重要作用。我们还提出了一种新颖的验证方案，用于评估损坏模式估计的性能。通过涵盖计算机视觉和自然语言处理等多个领域，我们提出的方法在鲁棒性和损坏模式估计方面得到了广泛评估。