aiqbench analysis
This research presents SpecRegMatch, a semi-supervised regression method designed to reduce computational costs when predicting vehicle interior noise using scarce labeled data. By leveraging consistency regularization and information maximization within a single model architecture, the approach achieves state-of-the-art performance without the overhead of training multiple models or extensive data sampling. The technique offers a practical pathway for deploying AI-driven acoustic prediction systems in automotive applications where data acquisition is expensive.
SpecRegMatch: Robust Semi-Supervised Regression for Vehicle Interior Noise Prediction
arXiv:2609.34111v1 Announce Type: new Abstract: The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive industry. However, the collection of labeled data for training models in this context involves significant costs. Previous studies in semi-supervised regression (SSR) have effectively mitigated the reliance on labeled data by incorporating unlabeled data. Nonetheless, these approaches often introduce a high computational cost due to the training of multiple models and data sampling. This study introduces SpecRegMatch, a novel SSR method aimed at addressing the computational cost associated with training by leveraging a single model, thus eliminating the need for multiple data samplings. SpecRegMatch integrates consistency regularization and information maximization to robustly train the model, achieved through various augmentations applied to both the embedding vectors and predicted values. Experimental results demonstrate that SpecRegMatch achieves state-of-the-art performance across various scenarios, even when using a single model. It attains a remarkable performance, as indicated by an R^2 score of 0.434. This is especially noteworthy in scenarios where labeled data is scarce. You can access the code for our proposed method at https://github.com/sejin-sim/SpecRegMatch.
Abstract:The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive industry. However, the collection of labeled data for training models in this context involves significant costs. Previous studies in semi-supervised regression (SSR) have effectively mitigated the reliance on labeled data by incorporating unlabeled data. Nonetheless, these approaches often introduce a high computational cost due to the training of multiple models and data sampling. This study introduces SpecRegMatch, a novel SSR method aimed at addressing the computational cost associated with training by leveraging a single model, thus eliminating the need for multiple data samplings. SpecRegMatch integrates consistency regularization and information maximization to robustly train the model, achieved through various augmentations applied to both the embedding vectors and predicted values. Experimental results demonstrate that SpecRegMatch achieves state-of-the-art performance across various scenarios, even when using a single model. It attains a remarkable performance, as indicated by an R^2 score of 0.434. This is especially noteworthy in scenarios where labeled data is scarce. You can access the code for our proposed method at this https URL.
Published in IEEE Access, vol. 12, pp. 60-72, 2024
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.34111 [cs.AI]
(or arXiv:2609.34111v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.34111 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
IEEE Access, vol. 12, pp. 60-72, 2024
https://doi.org/10.1109/ACCESS.2023.3347804 Focus to learn more DOI(s) linking to related resources
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