We propose two generic methods for improving semi-supervised learning (SSL).
The first integrates weight perturbation (WP) into existing "consistency
regularization" (CR) based methods. We implement WP by leveraging variational
Bayesian inference (VBI). The second method proposes a novel consistency loss
called "maximum uncertainty regularization" (MUR). While most consistency
losses act on perturbations in the vicinity of each data point, MUR actively
searches for "virtual" points situated beyond this region that cause the most
uncertain class predictions. This allows MUR to impose smoothness on a wider
area in the input-output manifold. Our experiments show clear improvements in
classification errors of various CR based methods when they are combined with
VBI or MUR or both.

本文提出了两种半监督学习的方法，第一种是将权重扰动方法（WP）应用到现有的一致性正则化（CR）方法中，第二种方法提出了一种新的一致性损失函数叫做 “最大不确定性正则化”（MUR），实验结果表明，将 VBI 或 MUR 或两种方法结合使用可以明显提高各种 CR 方法的分类误差。