Manipulating data, such as weighting data examples or augmenting with new
instances, has been increasingly used to improve model training. Previous work
has studied various rule- or learning-based approaches designed for specific
types of data manipulation. In this work, we propose a new method that supports
learning different manipulation schemes with the same gradient-based algorithm.
Our approach builds upon a recent connection of supervised learning and
reinforcement learning (RL), and adapts an off-the-shelf reward learning
algorithm from RL for joint data manipulation learning and model training.
Different parameterization of the "data reward" function instantiates different
manipulation schemes. We showcase data augmentation that learns a text
transformation network, and data weighting that dynamically adapts the data
sample importance. Experiments show the resulting algorithms significantly
improve the image and text classification performance in low data regime and
class-imbalance problems.

本文介绍了一种新的方法，它支持使用相同的梯度算法学习不同的数据操作方案。这种方法基于监督学习和强化学习之间的联系，并调整来自强化学习的现成奖励学习算法，用于联合数据操作学习和模型训练。通过学习文本转换网络和动态适应数据样本重要性来展示数据扩充和数据加权，实验表明所得到的算法明显提高了图像和文本分类的性能。