• Journal of Internet of Things and Convergence
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• v.4 no.2
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• pp.1-6
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• 2018

Techniques for generating new sample data from higher dimensional data such as images have been utilized variously for speech synthesis, image conversion and image restoration. This paper adopts Progressive Growing of Generative Adversarial Networks(PG-GANs) as an implementation model to generate high-resolution images and to enhance variation of the generated images, and applied it to fashion image data. PG-GANs allows the generator and discriminator to progressively learn at the same time, continuously adding new layers from low-resolution images to result high-resolution images. We also proposed a Mini-batch Discrimination method to increase the diversity of generated data, and proposed a Sliced Wasserstein Distance(SWD) evaluation method instead of the existing MS-SSIM to evaluate the GAN model.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.9-18
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• 2021

Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Fréchet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).

• International Journal of Contents
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• v.16 no.2
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• pp.8-18
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• 2020

Data is critical in deep learning but the scarcity of data often occurs in research, especially in the preparation of the paired training data. In this paper, document image binarization with unpaired data is studied by introducing adversarial learning, excluding the need for supervised or labeled datasets. However, the simple extension of the previous unpaired training to binarization inevitably leads to poor performance compared to paired data training. Thus, a new deep learning approach is proposed by introducing a multi-diversity of higher quality generated images. In this paper, a two-stage model is proposed that comprises the generative adversarial network (GAN) followed by the U-net network. In the first stage, the GAN uses the unpaired image data to create paired image data. With the second stage, the generated paired image data are passed through the U-net network for binarization. Thus, the trained U-net becomes the binarization model during the testing. The proposed model has been evaluated over the publicly available DIBCO dataset and it outperforms other techniques on unpaired training data. The paper shows the potential of using unpaired data for binarization, for the first time in the literature, which can be further improved to replace paired data training for binarization in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.31-36
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• 2019

Unsupervised neural networks have not caught enough attention until Generative Adversarial Network (GAN) was proposed. By using both the generator and discriminator networks, GAN can extract the main characteristic of the original dataset and produce new data with similarlatent statistics. However, researchers understand fully that training GAN is not easy because of its unstable condition. The discriminator usually performs too good when helping the generator to learn statistics of the training datasets. Thus, the generated data is not compelling. Various research have focused on how to improve the stability and classification accuracy of GAN. However, few studies delve into how to improve the training efficiency and to save training time. In this paper, we propose a novel optimizer, named FAST-ADAM, which integrates the Lookahead to ADAM optimizer to train the generator of a semi-supervised generative adversarial network (SSGAN). We experiment to assess the feasibility and performance of our optimizer using Canadian Institute For Advanced Research - 10 (CIFAR-10) benchmark dataset. From the experiment results, we show that FAST-ADAM can help the generator to reach convergence faster than the original ADAM while maintaining comparable training accuracy results.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.497-500
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• 2022

High-quality images and videos are being generated as technologies for deep learning-based image style translation and conversion of static images into dynamic images have developed. However, it takes a lot of time and resources to manually transform images, as well as professional knowledge due to the difficulty of natural image transformation. Therefore, in this paper, we study natural style mixing through a style conversion network using GAN and natural dynamic image generation using the First Order Motion Model network (FOMM).

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.547-557
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• 2019

To extract the depth map from a single image, a number of CNN-based deep learning methods have been performed in recent research. In this study, the GAN structure of Pix2Pix is maintained. this model allows to converge well, because it has the structure of the generator and the discriminator. But the convolution in this model takes a long time to compute. So we change the convolution form in the generator to a depthwise convolution to improve the speed while preserving the result. Thus, the seven down-sizing convolutional hidden layers in the generator U-Net are changed to depthwise convolution. This type of convolution decreases the number of parameters, and also speeds up computation time. The proposed model shows similar depth map prediction results as in the case of the existing structure, and the computation time in case of a inference is decreased by 64%.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.9 no.2
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• pp.1-9
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• 2005

Background: Liu-Yuan-Lei(陸淵雷) said that a medical record is both the marks of treatments and arts made by a excellent practitioner and the essence of TCM(Traditional Chinese Medicine). Jiang-Guan(江瓘) also said that reading medical records is one of the best way to develop one’s abilities If curing a disease without perfect clinical practice. Objectives: study on the special treatment about metrostaxis(崩漏) based on the Yi-Da-Gan(易大艮)’s medical records. and study on the differentiation of abnormal symptoms and signs about cold syndrome with pesudo-heat(眞寒假熱) based on the Yu-Chang(喩昌)'s medical records. Methods: First, read and study the medical records on metrostaxis(崩漏) of that made a profound study by Yi-Da-Gan(易大艮) and cold syndrome with pesudo-heat(眞寒假熱) of that made a profound study by Yu-Chang(喩昌). The next, write a paper on results and conclusions. Results and Conclusions: First, Yi-Da-Gan(易大艮) insist that must control the Qi under the blood disease conditions, taking the case of metrostaxis(崩漏). Secondly, we must study more on estimating the changing condition of Qi and the blood as time goes by, also study on the pulse and pulse condition in the four seasons(四時脈). Thirdly, Yu-Chang(喩昌) insist that be more careful in differentiation of symptoms and signs, taking the case of cold syndrome with pesudo-heat(眞寒假熱). Fourthly, Yu-Chang(喩昌) give an example that in condition of cold syndrome with pesudo-heat(眞寒假熱), sometimes, the pulse and pulse condition can be strong.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.257-264
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• 2019

Recently, deep neural network based approaches have shown a good performance for various fields of natural language processing. A huge amount of training data is essential for building a deep neural network model. However, collecting a large size of training data is a costly and time-consuming job. A data augmentation is one of the solutions to this problem. The data augmentation of text data is more difficult than that of image data because texts consist of tokens with discrete values. Generative adversarial networks (GANs) are widely used for image generation. In this work, we generate sentimental texts by using one of the GANs, CS-GAN model that has a discriminator as well as a classifier. We evaluate the usefulness of generated sentimental texts according to various measurements. CS-GAN model not only can generate texts with more diversity but also can improve the performance of its classifier.

• The Journal of Bigdata
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• v.5 no.2
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• pp.69-76
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• 2020

This study was conducted with the support of the Information and Communication Technology Promotion Center, funded by the government (Ministry of Science and ICT) in 2019. Highly pathogenic avian influenza (HPAI) is an acute infectious disease of birds caused by highly pathogenic avian influenza virus infection, causing serious damage to poultry such as chickens and ducks. High pathogenic avian influenza (HPAI) is caused by focusing on winter rather than year-round, and sometimes does not occur at all during a certain period of time. Due to these characteristics of HPAI, there is a problem that does not accumulate enough actual data. In this paper study, GAN network was utilized to generate actual similar data containing missing values and the process is introduced. The results of this study can be used to measure risk by generating realistic simulation data for certain times when HPAI did not occur.

• The Journal of Korean Medical History
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• v.26 no.1
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• pp.87-96
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• 2013

This study confirmed that a doctor named Lee, Seok-gan whose name has been widely known but whose real identity has remained unclear, was an active Confucian doctor in the 16th century. In addition, through the newly discovered "Daeyakbu" among his family line, writings, and relics that have been handed down in a family, this study looked into his medical philosophy and medicine culture. The author of "Ieseokgangyeongheombang"(Medical Book by Lee, Seok-gan(李石澗), Seok-gan is the same person as an active famous doctor Lee, Seok-gan(李碩幹, 1509-1574) in the 16th century. Such a fact can be confirmed through "Samuiilheombang", "Sauigyeongheombang" and the newly opened "Ieseokgangyeongheombang". Lee, Seok-gan was born in the 4th ruling year of king Jungjong (1509) and was active as a doctor until the 7th ruling year of king Seonjo(1547); his first name is Jungim with the pen name-Chodang, and he used a doctor name of 'Seokgan.' He was known as a divine doctor, and there have been left lots of anecdotes in relation with Lee, Seok-gan. Legend has it that Seokgan went to China to give treatment to the empress, and a heavenly peach pattern drinking cup and a house, which the emperor bestowed on Seokgan in return for his great services, still have remained up to the present. Usually, Seokgan interacted with Toegye Lee Hwang and his literary persons, and with his excellent medical skills, Seokgan once gave treatment to Toegye at the time of his death free of charge. His medical skills have been handed down in his family, and his descendant Lee, Ui-tae(around 1700) compiled a medical book titled "Gyeongheombangwhipyeon(經驗方彙編)". Out of Lee, Seok-gan's keepsakes which were donated to Sosu museums by his descendant family, 4 sorts of 'Gwabu'(writings of fruit trees) including "Daeyakbu" were discovered. It's rare to find a literary work left by a medical figure like this, so these discoveries have a deep meaning even from a medicine culture level. Particularly, "Daeyakbu" includes the typical "Uigukron". The "Uigukron", which develops its story by contrasting politics with medicine, has a unique writing style as one of the representative explanatory methods of scholars' position during the Joseon Dynasty; in addition, the distinctive feature of "Uigukron" is that it was created in the form of 'Gabu' other than a prose.