Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited labeled dataset for training. The effectiveness of self-training heavily relies on the accuracy of these pseudo-labels. In this paper, we introduce doubly robust self-training, a novel semi-supervised algorithm that provably balances between two extremes. When the pseudo-labels are entirely incorrect, our method reduces to a training process solely using labeled data. Conversely, when the pseudo-labels are completely accurate, our method transforms into a training process utilizing all pseudo-labeled data and labeled data, thus increasing the effective sample size. Through empirical evaluations on both the ImageNet dataset for image classification and the nuScenes autonomous driving dataset for 3D object detection, we demonstrate the superiority of the doubly robust loss over the standard self-training baseline.

翻译：自训练是解决半监督学习问题的重要技术。它通过生成伪标签，并将其与有限的标注数据集结合来进行训练，从而利用无标注数据。自训练的有效性很大程度上依赖于这些伪标签的准确性。本文提出双稳健自训练，这是一种新颖的半监督算法，能够在两个极端情况之间实现可证明的平衡。当伪标签完全错误时，我们的方法退化为仅使用标注数据的训练过程；反之，当伪标签完全准确时，我们的方法转变为利用所有伪标注数据和标注数据进行的训练过程，从而增加有效样本量。通过在ImageNet数据集上的图像分类任务和nuScenes自动驾驶数据集上的三维目标检测任务的实证评估，我们证明了双稳健损失相比标准自训练基线的优越性。