It is critical that the models pay attention not only to accuracy but also to the certainty of prediction. Uncertain predictions of deep models caused by noisy data raise significant concerns in trustworthy AI areas. To explore and handle uncertainty due to intrinsic data noise, we propose a novel method called ALUM to simultaneously handle the model uncertainty and data uncertainty in a unified scheme. Rather than solely modeling data uncertainty in the ultimate layer of a deep model based on randomly selected training data, we propose to explore mined adversarial triplets to facilitate data uncertainty modeling and non-parametric uncertainty estimations to compensate for the insufficiently trained latent model layers. Thus, the critical data uncertainty and model uncertainty caused by noisy data can be readily quantified for improving model robustness. Our proposed ALUM is model-agnostic which can be easily implemented into any existing deep model with little extra computation overhead. Extensive experiments on various noisy learning tasks validate the superior robustness and generalization ability of our method. The code is released at https://github.com/wwzjer/ALUM.

翻译：模型不仅应关注准确性，还需关注预测的确定性。由噪声数据引起的深度模型不确定预测，在可信人工智能领域引发了重大关注。为探索并处理由内在数据噪声引发的不确定性，我们提出一种名为ALUM的新方法，在统一框架下同时处理模型不确定性与数据不确定性。不同于仅在深度模型末层基于随机选取的训练数据进行数据不确定性建模，我们提出利用挖掘的对抗三元组促进数据不确定性建模，并通过非参数不确定性估计补偿训练不足的隐式模型层。由此，可便捷量化由噪声数据引发的关键数据不确定性与模型不确定性，从而提升模型鲁棒性。所提出的ALUM具有模型无关性，可轻松集成至任意现有深度模型，且仅需少量额外计算开销。在多种噪声学习任务上的大量实验验证了本方法的优越鲁棒性与泛化能力。代码已开源至https://github.com/wwzjer/ALUM。