  Example weighting algorithm is an effective solution to the training bias
problem, however, most previous typical methods are usually limited to human
knowledge and require laborious tuning of hyperparameters. In this paper, we
propose a novel example weighting framework called Learning to Auto Weight
(LAW). The proposed framework finds step-dependent weighting policies
adaptively, and can be jointly trained with target networks without any
assumptions or prior knowledge about the dataset. It consists of three key
components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge
searching space in a complete training process; Duplicate Network Reward (DNR)
gives more accurate supervision by removing randomness during the searching
process; Full Data Update (FDU) further improves the updating efficiency.
Experimental results demonstrate the superiority of weighting policy explored
by LAW over standard training pipeline. Compared with baselines, LAW can find a
better weighting schedule which achieves much more superior accuracy on both
biased CIFAR and ImageNet.
