  Learning with labels noise has gained significant traction recently due to
the sensitivity of deep neural networks under label noise under common loss
functions. Losses that are theoretically robust to label noise, however, often
makes training difficult. Consequently, several recently proposed methods, such
as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to
learn a weighting function that downweights samples that are likely to have
corrupted labels under the meta-learning framework. However, obtaining such a
set of clean samples is not always feasible in practice. In this paper, we
analytically show that one can easily train MW-Net without access to clean
samples simply by using a loss function that is robust to label noise, such as
mean absolute error, as the meta objective to train the weighting network. We
experimentally show that our method beats all existing methods that do not use
clean samples and performs on-par with methods that use gold samples on
benchmark datasets across various noise types and noise rates.
