Dual-Domain Fusion for Semi-Supervised Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911383327604736 |
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| author | Jalonen, Tuomas Al-Sa'd, Mohammad Kiranyaz, Serkan Gabbouj, Moncef |
| author_facet | Jalonen, Tuomas Al-Sa'd, Mohammad Kiranyaz, Serkan Gabbouj, Moncef |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11824 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-Domain Fusion for Semi-Supervised Learning Jalonen, Tuomas Al-Sa'd, Mohammad Kiranyaz, Serkan Gabbouj, Moncef Machine Learning Artificial Intelligence Signal Processing 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. |
| title | Dual-Domain Fusion for Semi-Supervised Learning |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2503.11824 |