  Modern deep neural networks can easily overfit to biased training data
containing corrupted labels or class imbalance. Sample re-weighting methods are
popularly used to alleviate this data bias issue. Most current methods,
however, require to manually pre-specify the weighting schemes as well as their
additional hyper-parameters relying on the characteristics of the investigated
problem and training data. This makes them fairly hard to be generally applied
in practical scenarios, due to their significant complexities and inter-class
variations of data bias situations. To address this issue, we propose a
meta-model capable of adaptively learning an explicit weighting scheme directly
from data. Specifically, by seeing each training class as a separate learning
task, our method aims to extract an explicit weighting function with sample
loss and task/class feature as input, and sample weight as output, expecting to
impose adaptively varying weighting schemes to different sample classes based
on their own intrinsic bias characteristics. Synthetic and real data
experiments substantiate the capability of our method on achieving proper
weighting schemes in various data bias cases, like the class imbalance,
feature-independent and dependent label noise scenarios, and more complicated
bias scenarios beyond conventional cases. Besides, the task-transferability of
the learned weighting scheme is also substantiated, by readily deploying the
weighting function learned on relatively smaller-scale CIFAR-10 dataset on much
larger-scale full WebVision dataset. A performance gain can be readily achieved
compared with previous SOAT ones without additional hyper-parameter tuning and
meta gradient descent step. The general availability of our method for multiple
robust deep learning issues, including partial-label learning, semi-supervised
learning and selective classification, has also been validated.
