A Weak Signal Learning Dataset and Its Baseline Method

• URL: http://arxiv.org/abs/2512.23160v1
• Date: Mon, 29 Dec 2025 02:48:47 GMT
• Title: A Weak Signal Learning Dataset and Its Baseline Method
• Abstract summary: Weak signal learning is a common challenge in many fields like fault diagnosis, medical imaging, and autonomous driving.<n>We construct the first specialized dataset for weak signal feature learning, containing 13,158 spectral samples.<n>Experiments show our method achieves higher accuracy and robustness in handling weak signals, high noise, and extreme class imbalance.
• Score: 0.41560960922138807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weak signal learning (WSL) is a common challenge in many fields like fault diagnosis, medical imaging, and autonomous driving, where critical information is often masked by noise and interference, making feature identification difficult. Even in tasks with abundant strong signals, the key to improving model performance often lies in effectively extracting weak signals. However, the lack of dedicated datasets has long constrained research. To address this, we construct the first specialized dataset for weak signal feature learning, containing 13,158 spectral samples. It features low SNR dominance (over 55% samples with SNR below 50) and extreme class imbalance (class ratio up to 29:1), providing a challenging benchmark for classification and regression in weak signal scenarios. We also propose a dual-view representation (vector + time-frequency map) and a PDVFN model tailored to low SNR, distribution skew, and dual imbalance. PDVFN extracts local sequential features and global frequency-domain structures in parallel, following principles of local enhancement, sequential modeling, noise suppression, multi-scale capture, frequency extraction, and global perception. This multi-source complementarity enhances representation for low-SNR and imbalanced data, offering a novel solution for WSL tasks like astronomical spectroscopy. Experiments show our method achieves higher accuracy and robustness in handling weak signals, high noise, and extreme class imbalance, especially in low SNR and imbalanced scenarios. This study provides a dedicated dataset, a baseline model, and establishes a foundation for future WSL research.

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