## 2 Related Works  
Defect detection using computer vision has been widely studied for automated inspection systems. It has gradually displaced the traditional manual methods. Also, the study of the defect detection method using deep learning is accelerating the replacement of the traditional manual methods. As previously stated in Section 1, studies of deep learning-based defect detection are categorized into two groups: supervised and unsupervised learning-based methods. However, supervised learning-based method has the problem of limited defected data. To overcome this problem, there have been various studies on detecting defects using unsupervised learning-based methods.  
The studies using autoencoders have mainly conducted for unsupervised defect detection method. These studies use differences between original image and restored image from the autoencoders to detect defects. [PERSON] et al. [5] adopted the multi-scale convolutional denoising autoencoder (MSCDAE) architecture. They use the various scales of the image generated by the Gaussian pyramid and utilize the salt-and-pepper noise model to the input image to train the denoising autoencoders of various sizes. They try to detect defects by combining outputs of the autoencoders with various sizes. This method shows 80% accuracy and 0.65 F-Score on fabric datasets.  
[PERSON] et al. [7] used perceptual loss function to get better performance of the autoencoder in defect detection. They note that existing methods lead to large residuals in edge regions that have slight localization inaccuracies. To solve this problem, they proposed a method using perceptual loss function based on structure similarity to detect defects. The loss function measures luminance, contrast, and structure between image patches. Besides that, there are various approaches to detect defects based on unsupervised learning without using autoencoders. These studies tried to detect defects by using wavelet transformation [8; 9; 10] or Gabor filter [11; 12].  
Also, there are studies using an unsupervised and a supervised manner simultaneously. [PERSON] et al. [6] adopt Fisher criterion-based stacked denoising autoencoder (FCSDA). They designed a Fisher criterion-based loss function in the feature space to overcome the limited defected data problem. They trained two autoencoders and use them as a classification network and a segmentation network. The classification network is trained in a supervised manner with labeled dataset, and the segmentation network is trained in an unsupervised manner. If the classification network judges an image patch as a defect, the segmentation network locates the defect.  
The aforementioned studies about unsupervised defect detection have focused on the loss functions to train the autoencoders suitably or ensemble the results of the various autoencoders to get better performance. They all trained denoising autoencoders and used them for defect detection. However, they did not consider much about adding noise to the input of the denoising autoencoders in the training phase.  
They use only typical type of noise, such as Gaussian noise or salt-and-pepper noise. In our work, we focused on the noise used in the training phase. We added not only Gaussian noise but also generated synthetic defects to the input of the denoising autoencoders for training. Our method achieved significant performance improvement in real-world patterned fabric data.