# Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model

Source: [Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model](<https://www.sota2.com/research/paper/68fa72f01e209131cea5963f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:37:39.153Z

Authors: Masahito Toba, Seiichi Uchida, Hideaki Hayashi

Published: 2024-04-15T08:52:51.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.09585>)

[PDF](<https://arxiv.org/pdf/2404.09585.pdf>)