Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization

We propose two generic methods for improving semisupervised 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.

Authors

• Kien Do Applied Artificial Intelligence Institute, Deakin University
• Truyen Tran Applied Artificial Intelligence Institute, Deakin University
• Svetha Venkatesh Deakin University

Keywords

No keywords are indexed for this paper.