Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
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DOI：
10.1109/icit58465.2023.10143099
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Adrian Li;Elisa Bertino;Rih-Teng Wu;Ting Wu
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文献类型：
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作者：
Adrian Li;Elisa Bertino;Rih-Teng Wu;Ting Wu
深度学习（DL）技术对于图像中的缺陷检测非常有效。然而，训练DL分类模型需要大量的标记数据，这通常是昂贵的收集。在许多情况下，不仅可用的训练数据是有限的，而且可能不平衡。在本文中，我们提出了一种新的域自适应（DA）方法，通过转移从用于类似学习任务的现有源数据集获得的知识，来解决目标学习任务的标记训练数据稀缺的问题。我们的方法适用于源数据集和可用于目标学习任务的数据集具有相同或不同特征空间的场景。我们将我们的DA方法与基于自动编码器的数据增强方法结合起来，以解决目标数据集不平衡的问题。联合收割机。我们评估我们的结合使用图像数据的晶圆缺陷预测的方法。实验结果表明，当目标数据集中的标记样本数量非常少且目标数据集不平衡时，该算法具有优于其他算法的上级性能。
Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced.