State-of-the-art (SOTA) semi-supervised learning techniques, such as FixMatch and it's variants, have demonstrated impressive performance in classification tasks. However, these methods are not directly applicable to regression tasks. In this paper, we present RankUp, a simple yet effective approach that adapts existing semi-supervised classification techniques to enhance the performance of regression tasks. RankUp achieves this by converting the original regression task into a ranking problem and training it concurrently with the original regression objective. This auxiliary ranking classifier outputs a classification result, thus enabling integration with existing semi-supervised classification methods. Moreover, we introduce regression distribution alignment (RDA), a complementary technique that further enhances RankUp's performance by refining pseudo-labels through distribution alignment. Despite its simplicity, RankUp, with or without RDA, achieves SOTA results in across a range of regression benchmarks, including computer vision, audio, and natural language processing tasks. Our code and log data are open-sourced at https://github.com/pm25/semi-supervised-regression.

翻译：当前最先进的半监督学习方法（如FixMatch及其变体）在分类任务中展现出卓越性能，但这些方法无法直接应用于回归任务。本文提出RankUp，一种简单而有效的方法，通过改造现有半监督分类技术来提升回归任务的性能。RankUp的核心策略是将原始回归任务转化为排序问题，并与原始回归目标协同训练。该辅助排序分类器输出分类结果，从而能够与现有半监督分类方法无缝集成。此外，我们提出回归分布对齐技术，通过分布对齐优化伪标签，进一步提升RankUp的性能。尽管方法简洁，无论是否结合RDA，RankUp在计算机视觉、音频和自然语言处理等多个回归基准测试中均取得了最先进的结果。我们的代码与实验日志已开源：https://github.com/pm25/semi-supervised-regression。