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Detecting Mode Drop and Collapse in GANs Using Simplified Frèchet Distance
Chung-Il Kim, Seungwon Jung, Jihoon Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2019.46.10.1012
Even though generative adversarial network (GAN) is an excellent model for generating data based on the estimation of real data distribution by of two adversarial learning network, this model often suffers from mode drop that does not learn distribution during learning, or mode collapse that generates only one or very few distribution samples. Most studies to detect these problems have used well-balanced data or additional neural network models. In this paper, we propose a method to detect mode drop and collapse by using a simplified Frèchet distance, which does not require any additional model or well-balanced data. Through various experiments, we showed that our proposed distance metric detected mode drop and collapse more accurately than any other metrics used in GANs.
A Twitter News-Classification Scheme Using Semantic Enrichment of Word Features
Seonmi Ji, Jihoon Moon, Hyeonwoo Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2018.45.10.1045
Recently, with the popularity of Twitter as a news platform, many news articles are generated, and various kinds of information and opinions about them spread out very fast. But since an enormous amount of Twitter news is posted simultaneously, users have difficulty in selectively browsing for news related to their interests. So far, many works have been conducted on how to classify Twitter news using machine learning and deep learning. In general, conventional machine learning schemes show data sparsity and semantic gap problems, and deep learning schemes require a large amount of data. To solve these problems, in this paper, we propose a Twitter news-classification scheme using semantic enrichment of word features. Specifically, we first extract the features of Twitter news data using the Vector Space Model. Second, we enhance those features using DBpedia Spotlight. Finally, we construct a topic-classification model based on various machine learning techniques and demonstrate by experiments that our proposed model is more effective than other traditional methods.
Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data
Jihoon Moon, Jinwoong Park, Sanghoon Han, Eenjun Hwang
http://doi.org/10.5626/JOK.2017.44.9.954
A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.
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