  Current deep neural networks (DNNs) can easily overfit to biased training
data with corrupted labels or class imbalance. Sample re-weighting strategy is
commonly used to alleviate this issue by designing a weighting function mapping
from training loss to sample weight, and then iterating between weight
recalculating and classifier updating. Current approaches, however, need
manually pre-specify the weighting function as well as its additional
hyper-parameters. It makes them fairly hard to be generally applied in practice
due to the significant variation of proper weighting schemes relying on the
investigated problem and training data. To address this issue, we propose a
method capable of adaptively learning an explicit weighting function directly
from data. The weighting function is an MLP with one hidden layer, constituting
a universal approximator to almost any continuous functions, making the method
able to fit a wide range of weighting functions including those assumed in
conventional research. Guided by a small amount of unbiased meta-data, the
parameters of the weighting function can be finely updated simultaneously with
the learning process of the classifiers. Synthetic and real experiments
substantiate the capability of our method for achieving proper weighting
functions in class imbalance and noisy label cases, fully complying with the
common settings in traditional methods, and more complicated scenarios beyond
conventional cases. This naturally leads to its better accuracy than other
state-of-the-art methods.
