In semi-supervised semantic segmentation (SSS), weak-to-strong consistency regularization techniques are widely utilized in recent works, typically combined with input-level and feature-level perturbations. However, the integration between weak-to-strong consistency regularization and network perturbation has been relatively rare. We note several problems with existing network perturbations in SSS that may contribute to this phenomenon. By revisiting network perturbations, we introduce a new approach for network perturbation to expand the existing weak-to-strong consistency regularization for unlabeled data. Additionally, we present a volatile learning process for labeled data, which is uncommon in existing research. Building upon previous work that includes input-level and feature-level perturbations, we present MLPMatch (Multi-Level-Perturbation Match), an easy-to-implement and efficient framework for semi-supervised semantic segmentation. MLPMatch has been validated on the Pascal VOC and Cityscapes datasets, achieving state-of-the-art performance. Code is available from https://github.com/LlistenL/MLPMatch.

本研究针对半监督语义分割中弱-强一致性正则化技术与网络扰动结合不足的问题，提出了一种新的网络扰动方法，以扩展现有的弱-强一致性正则化，特别是针对未标记数据。同时，我们引入了不稳定的标记数据学习过程，开发了一个名为MLPMatch的高效框架，验证其在Pascal VOC和Cityscapes数据集上的先进性能，推动了该领域的发展。

重新审视网络扰动在半监督语义分割中的应用