Pseudo-label Learning with Calibrated Confidence Using an Energy-based
  Model
Masahito Toba Seiichi Uchida Hideaki Hayashi
In pseudo-labeling (PL), which is a type of semi-supervised learning,
pseudo-labels are assigned based on the confidence scores provided by the
classifier; therefore, accurate confidence is important for successful PL. In
this study, we propose a PL algorithm based on an energy-based model (EBM),
which is referred to as the energy-based PL (EBPL). In EBPL, a neural
network-based classifier and an EBM are jointly trained by sharing their
feature extraction parts. This approach enables the model to learn both the
class decision boundary and input data distribution, enhancing confidence
calibration during network training. The experimental results demonstrate that
EBPL outperforms the existing PL method in semi-supervised image classification
tasks, with superior confidence calibration error and recognition accuracy.