  Robust loss functions are essential for training accurate deep neural
networks (DNNs) in the presence of noisy (incorrect) labels. It has been shown
that the commonly used Cross Entropy (CE) loss is not robust to noisy labels.
Whilst new loss functions have been designed, they are only partially robust.
In this paper, we theoretically show by applying a simple normalization that:
any loss can be made robust to noisy labels. However, in practice, simply being
robust is not sufficient for a loss function to train accurate DNNs. By
investigating several robust loss functions, we find that they suffer from a
problem of underfitting. To address this, we propose a framework to build
robust loss functions called Active Passive Loss (APL). APL combines two robust
loss functions that mutually boost each other. Experiments on benchmark
datasets demonstrate that the family of new loss functions created by our APL
framework can consistently outperform state-of-the-art methods by large
margins, especially under large noise rates such as 60% or 80% incorrect
labels.
