  Accurately annotating large scale dataset is notoriously expensive both in
time and in money. Although acquiring low-quality-annotated dataset can be much
cheaper, it often badly damages the performance of trained models when using
such dataset without particular treatment. Various methods have been proposed
for learning with noisy labels. However, most methods only handle limited kinds
of noise patterns, require auxiliary information or steps (e.g. , knowing or
estimating the noise transition matrix), or lack theoretical justification. In
this paper, we propose a novel information-theoretic loss function,
$\mathcal{L}_{DMI}$, for training deep neural networks robust to label noise.
The core of $\mathcal{L}_{DMI}$ is a generalized version of mutual information,
termed Determinant based Mutual Information (DMI), which is not only
information-monotone but also relatively invariant. \emph{To the best of our
knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably
robust to instance-independent label noise, regardless of noise pattern, and it
can be applied to any existing classification neural networks straightforwardly
without any auxiliary information}. In addition to theoretical justification,
we also empirically show that using $\mathcal{L}_{DMI}$ outperforms all other
counterparts in the classification task on both image dataset and natural
language dataset include Fashion-MNIST, CIFAR-10, Dogs vs. Cats, MR with a
variety of synthesized noise patterns and noise amounts, as well as a
real-world dataset Clothing1M. Codes are available at
https://github.com/Newbeeer/L_DMI .
