## 2 Related Works
### Data Augmentation  
In the manufacturing industry, since the number of normal products far exceeds the number of defective products, it is difficult to secure a large quantity of defective data. If the number of defect patterns used in the model learning is significantly low, the model cannot sufficiently learn features to accurately classify these defects. This reduces the classification accuracy of the model. Additionally, the number of each defect pattern collected through industrial data is imbalanced because the frequency of occurrence for each defect pattern varies depending on the abnormality occurring in the process. To address this problem, various data augmentation methods are used to improve the performance of classifiers. Data augmentation is a method used to increase the size of the training set to be used for classifier learning by artificially generating a training sample. The generated samples should be as close to authentic as possible, and ideally should not allow us to determine whether they were artificially generated or not. Classical image data augmentation methods include resizing, rotating, or inverting the training data, as well as changing the brightness of, cropping, or enlarging the image. This is an effective data augmentation method for learning several image classifiers. However, some of these methods are unsuitable for use in classifying wafer map defect patterns because these methods destroy the characteristics that distinguish the defect patterns present on WBMs. For example, when the middle region of a WBM with a 'center' defect pattern is cropped and used for model learning, the classifier fails to fully learn the important features that distinguish 'none' from 'center.' As such, training the classifier by augmenting the data in a way that is not suitable for the characteristics of the data degrades the performance of the model. Therefore, in order to improve the performance of the classifier, it is important to select an appropriate data augmentation method. The following two strategies are widely known as methods for improving the classification performance of such imbalanced data. (1) Transfer learning [26]: Transfer learning is not a data augmentation method, but it is a way to improve the classification performance of imbalanced data. Transfer learning is a method that can be generalized to classify a small number of data using a pre-trained model with a large amount of data. [PERSON] et al. showed that in the case of a wafer map with a significant class imbalance problem, the transfer learning structure proposed in their paper identified defects better than a general CNN structure [27]. However, the disadvantage of transfer learning is that it requires a lot of effort to tune the hyperparameters to fit the model being used. (2) Sampling techniques: Another way to improve the classification performance is to use sampling techniques to address data imbalance. Two types of sampling methods are typically used. The first is the under-sampling method of reducing the number of relatively large classes compared to other classes to evenly match the overall ratio, and the other is the over-sampling method of increasing the data of insufficient classes compared to other classes. Under-sampling methods can be used to balance classes, but insufficient samples per class can result in the loss of useful characteristics for classifiers in learning class diversity. It could be alleviated by means of over-sampling methods. In the context of classifier training, it is crucial to consider not only the quantity but also the quality and diversity of data to prevent classifiers from being biased towards certain areas of the image. A classifier trained on a small amount of data may exhibit overfitting, causing it to be overly biased towards the given training data. Similarly, a large amount of training data alone does not guarantee generalization if the diversity of the data is lacking. Hence, it is essential to ensure the quality and diversity of data when securing data for classifier training.