• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.673-674
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• 2021

본 논문은 집중력 향상 놀이인 다른 그림 찾기를 자신이 좋아하는 주제를 배경으로 쉽게 생성할 수 있는 시스템을 제안한다. 아동기에 주로 진단이 되고 성인기까지 이어질 수 있는 주의력 결핍 과다활동 증후군(ADHD)을 조기에 예방하기 위해 본 논문에서는 선택한 그림의 일부분을 가지고 생성적 적대 신경망을 활용하여 새로운 물체를 생성해 낸 뒤 자연스럽게 원본 그림에 융화될 수 있도록 하는 것이 목표이다. 하나의 다른 그림 찾기 콘텐츠를 만드는 것은 포토샵과 같이 전문성을 가진 툴을 전문가가 오랜 시간 작업해야 하는 내용이다. 전문적인 기술이 필요한 작업 과정을 본 연구를 통해 일반인도 쉽게 작업할 수 있도록 하는 것을 최종 목표로 한다.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.36 no.2
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• pp.18-24
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• 2018

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.123-125
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• 2022

본 논문에서는 판별자를 활용하여 Image to Image translation(I2I) 분야에서 사용되는 적대적 생성 신경망(GAN)을 압축하는 방법을 제시한다. 우선, 잘 학습된 판별자와 생성자 사이의 adversarial loss 를 활용하여 생성자 내 필터들의 중요도 점수를 매겨준다. 그리고 생성자 내의 필터들을 중요도 점수를 기준으로 나열한 후 점수가 낮은 필터들을 제거하는 필터 프루닝을 한번 수행하여 적은 시간 비용으로 생성자를 압축한다. 마지막으로 지식 증류를 활용해 압축된 생성자를 학습시켜 기존의 생성자와 유사한 성능을 보이도록 하였다. 이 과정들을 통해 효과적이고 빠르게 GAN 모델을 압축할 수 있음을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.100-102
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• 2020

심층 신경망은 적대적인 공격으로 생성된 적대적 예제에 의해 쉽게 오작동할 수 있다. 이에 따라 다양한 방어 방법들이 제안되었으나, 더욱 강력한 적대적인 공격이 제안되어 방어 방법들을 무력화할 가능성은 존재한다. 이러한 가능성은 어떤 공격 범위 내의 적대적인 공격을 방어할 수 있다고 보장할 수 있는 인증된 방어(Certified defense) 방법의 필요성을 강조한다. 이에 본 논문은 인증된 방어 방법 중 가장 효과적인 방법의 하나로 알려진 구간 경계 전파(Interval Bound Propagation)의 성능을 향상하는 방법을 연구한다. 구체적으로, 우리는 기존의 구간 경계 전파 방법의 훈련 과정을 수정하는 방법을 제안하며, 이를 통해 기존 구간 경계 전파 방법의 훈련 시간을 유지하면서 성능을 향상할 수 있음을 보일 것이다. 우리가 제안한 방법으로 수행한 MNIST 데이터 셋에 대한 실험에서 우리는 기존 구간 경계 전파 방법 대비 인증 에러(Verified error)를 Large 모델에 대해서 1.77%, Small 모델에 대해서 0.96% 낮출 수 있었다.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.9-16
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• 2020

In the Image processing and computer vision, the problem of reconstructing from one image to another or generating a new image has been steadily drawing attention as hardware advances. However, the problem of computer-generated images also continues to emerge when viewed with human eyes because it is not natural. Due to the recent active research in deep learning, image generating and improvement problem using it are also actively being studied, and among them, the network called Generative Adversarial Network(GAN) is doing well in the image generating. Various models of GAN have been presented since the proposed GAN, allowing for the generation of more natural images compared to the results of research in the image generating. Among them, pix2pix is a conditional GAN model, which is a general-purpose network that shows good performance in various datasets. pix2pix is based on U-Net, but there are many networks that show better performance among U-Net based networks. Therefore, in this study, images are generated by applying various networks to U-Net of pix2pix, and the results are compared and evaluated. The images generated through each network confirm that the pix2pix model with Attention, R2, and Attention-R2 networks shows better performance than the existing pix2pix model using U-Net, and check the limitations of the most powerful network. It is suggested as a future study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.842-848
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• 2020

Music markets have entered the era of streaming. In order to select and propose music that suits the taste of music consumers, there is an active demand and research on an automatic music genre classification system. We propose a method to improve the accuracy of genre unclassified songs, which was a lack of the previous system, by using a generative adversarial network (GAN) to further develop the automatic voting system for deep learning music genre using Softmax proposed in the previous paper. In the previous study, if the spectrogram of the song was ambiguous to grasp the genre of the song, it was forced to leave it as an unclassified song. In this paper, we proposed a system that increases the accuracy of genre classification of unclassified songs by converting the spectrogram of unclassified songs into an easy-to-read spectrogram using GAN. And the result of the experiment was able to derive an excellent result compared to the existing method.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1197-1205
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• 2018

In this paper, we perform Single Image Super Resolution(SISR) for acquired images of EOTS or IRST from naval combat system. In order to conduct super resolution, we use Generative Adversarial Networks(GANs), which consists of a generative model to create a super-resolution image from the given low-resolution image and a discriminative model to determine whether the generated super-resolution image is qualified as a high-resolution image by adjusting various learning parameters. The learning parameters consist of a crop size of input image, the depth of sub-pixel layer, and the types of training images. Regarding evaluation method, we apply not only general image quality metrics, but feature descriptor methods. As a result, a larger crop size, a deeper sub-pixel layer, and high-resolution training images yield good performance.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.503-510
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• 2018

Restoring of damaged images is a fundamental problem that was attempted before digital image processing technology appeared. Various algorithms for reconstructing damaged images have been introduced. However, the results show inferior restoration results compared with manual restoration. Recent developments of DNN (Deep Neural Network) have introduced various studies that apply it to image restoration. However, if the wide area is damaged, it can not be solved by a general interpolation method. In this case, it is necessary to reconstruct the damaged area through contextual information of surrounding images. In this paper, we propose an image restoration network using a generative adversarial network (GAN). The proposed system consists of image generation network and discriminator network. The proposed network is verified through experiments that it is possible to recover not only the natural image but also the texture of the original image through the inference of the damaged area in restoring various types of images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.171-179
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• 2020

Recently, studies have shown that convolution neural networks are achieving the best performance in image classification, object detection, and image generation. Vision based defect inspection which is more economical than other defect inspection, is a very important for a factory automation. Although supervised anomaly detection algorithm has far exceeded the performance of traditional machine learning based method, it is inefficient for real industrial field due to its tedious annotation work, In this paper, we propose ADGAN, a unsupervised anomaly detection architecture using the variational autoencoder and the generative adversarial network which give great results in image generation task, and demonstrate whether the proposed network architecture identifies anomalous images well on MNIST benchmark dataset as well as our own welding defect dataset.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.