SpecRegMatch：用于车辆内部噪声预测的鲁棒半监督回归
SpecRegMatch: Robust Semi-Supervised Regression for Vehicle Interior Noise Prediction
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SpecRegMatch提出一种基于单模型、融合一致性正则化与信息最大化的半监督回归方法，用于低成本预测车辆内部噪声，在标记数据稀缺时达到R²=0.434的最优性能。
AI中文摘要：
人工智能的快速发展使得其在汽车行业中预测车辆内部噪声水平的应用日益增多。然而，在此背景下，为训练模型而收集标记数据涉及高昂的成本。以往在半监督回归（SSR）方面的研究通过纳入未标记数据有效减轻了对标记数据的依赖。尽管如此，这些方法往往因训练多个模型和进行数据采样而引入较高的计算成本。本研究提出了SpecRegMatch，一种新颖的SSR方法，旨在通过利用单一模型来解决与训练相关的计算成本，从而消除了多次数据采样的需要。SpecRegMatch整合了一致性正则化和信息最大化，通过对嵌入向量和预测值施加多种增强方式，实现模型的鲁棒训练。实验结果表明，即使使用单一模型，SpecRegMatch在多种场景下也达到了最先进的性能。其性能尤为显著，R²分数达到0.434。这在标记数据稀缺的场景中尤其值得注意。您可以通过此https URL访问我们提出的方法的代码。
英文摘要：
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.
