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
this https URL

本文提出了一种学习样本快速再加权方法（FSR），该方法通过历史记录学习构建代理奖励数据和特征共享以降低优化成本，无需额外奖励数据和昂贵的二阶计算，并在标签噪声鲁棒性和长尾识别方面达到具有竞争力的结果，同时显着提高了训练效率。