论文标题
半监督学习，具有变异的贝叶斯推断和最大的不确定性正则化
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
论文作者
论文摘要
我们提出了两种用于改进半监督学习（SSL）的通用方法。第一个将重量扰动（WP）集成到现有的“一致性正则化”（CR）方法中。我们通过利用变异贝叶斯推理（VBI）来实现WP。第二种方法提出了一种新的一致性损失，称为“最大不确定性正则化”（MUR）。尽管大多数一致性损失作用于每个数据点附近扰动的作用，但MUR积极地寻找位于该地区以外的“虚拟”点，这些点会导致最不确定的类预测。这使MUR可以在输入输出歧管中的更宽区域施加平滑度。我们的实验表明，当各种基于CR的方法与VBI或MUR或MUR或两者兼有的各种基于CR的方法的分类误差方面有明显的改善。
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.
