# Dual-Domain Fusion for Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2503.11824 · HTML: https://mlsanity.com/paper/2503.11824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods](https://mlsanity.com/paper/1903.11260/) (2019)
- [Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2211.16701/) (2022)
- [Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions](https://mlsanity.com/paper/2311.18547/) (2023)

## Abstract (arXiv)

Labeled time-series data is often expensive and difficult to obtain, making it challenging to train accurate machine learning models for real-world applications such as anomaly detection or fault diagnosis. The scarcity of labeled samples limits model generalization and leaves valuable unlabeled data underutilized. We propose Dual-Domain Fusion (DDF), a new model-agnostic semi-supervised learning (SSL) framework applicable to any time-series signal. DDF performs dual-domain training by combining the one-dimensional time-domain signals with their two-dimensional time-frequency representations and fusing them to maximize learning performance. Its tri-model architecture consists of time-domain, time-frequency, and fusion components, enabling the model to exploit complementary information across domains during training. To support practical deployment, DDF maintains the same inference cost as standard time-domain models by discarding the time-frequency and fusion branches at test time. Experimental results on two public fault diagnosis datasets demonstrate substantial accuracy improvements of 8-46% over widely used SSL methods FixMatch, MixMatch, Mean Teacher, Adversarial Training, and Self-training. These results show that DDF provides an effective and generalizable strategy for semi-supervised time-series classification.