  Training sample re-weighting is an effective approach for tackling data
biases such as imbalanced and corrupted labels. Recent methods develop
learning-based algorithms to learn sample re-weighting strategies jointly with
model training based on the frameworks of reinforcement learning and meta
learning. However, depending on additional unbiased reward data is limiting
their general applicability. Furthermore, existing learning-based sample
re-weighting methods require nested optimizations of models and weighting
parameters, which requires expensive second-order computation. This paper
addresses these two problems and presents a novel learning-based fast sample
re-weighting (FSR) method that does not require additional reward data. The
method is based on two key ideas: learning from history to build proxy reward
data and feature sharing to reduce the optimization cost. Our experiments show
the proposed method achieves competitive results compared to state of the arts
on label noise robustness and long-tailed recognition, and does so while
achieving significantly improved training efficiency. The source code is
publicly available at
https://github.com/google-research/google-research/tree/master/ieg.
