  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.
