  It is important to learn various types of classifiers given training data
with noisy labels. Noisy labels, in the most popular noise model hitherto, are
corrupted from ground-truth labels by an unknown noise transition matrix. Thus,
by estimating this matrix, classifiers can escape from overfitting those noisy
labels. However, such estimation is practically difficult, due to either the
indirect nature of two-step approaches, or not big enough data to afford
end-to-end approaches. In this paper, we propose a human-assisted approach
called Masking that conveys human cognition of invalid class transitions and
naturally speculates the structure of the noise transition matrix. To this end,
we derive a structure-aware probabilistic model incorporating a structure
prior, and solve the challenges from structure extraction and structure
alignment. Thanks to Masking, we only estimate unmasked noise transition
probabilities and the burden of estimation is tremendously reduced. We conduct
extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as
well as the industrial-level Clothing1M with agnostic noise structure, and the
results show that Masking can improve the robustness of classifiers
significantly.
