Academic Expert Search Method Using Importance and Quality of Papers

Lee, Seo-Hee; Park, Yun-jeong; Han, Jin-Su; Choi, Do Young; Lim, Jongtae; Bok, Kyoungsoo; Yoo, Jaesoo

doi:10.5392/jkca.2016.16.12.458

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Several studies are being focused on resolving these problems using augmentation techniques and batch sampling. 2 Our purpose in this paper is to solve these issues by training using only normal data, as one of several studies. 3 We haven chosen to use a reconstructive approach based on GAN-networks and we have evaluated especially a f-AnoGAN method that has been proposed first by Schleg 4 for medical imaging.…”

Section: Introductionmentioning

confidence: 99%

f-AnoGAN for non-destructive testing in industrial anomaly detection

Sliti

Devanne

Kohler

et al. 2023

Sixteenth International Conference on Quality Control by Artificial Vision

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Being able to identify defects is an essential step during manufacturing processes. Yet, not all defects are necessarily known and sufficiently well described in the databases images. The challenge we address in this paper is to detect any defect by fitting a model using only normal samples of industrial parts. For this purpose, we propose to test fast AnoGAN (f-AnoGAN) approach based on a generative adversarial network (GAN). The method is an unsupervised learning algorithm, that contains two phases; first, we train a generative model using only normal images, which proposes a fast mapping of new data into the latent space. Second, we add and train an encoder to reconstruct images. The anomaly detection is defined by the reconstruction error between the defected data and the reconstructed ones, and the residual error of the discriminator. For our experiments, we use two sets of industrial data; the MVTec Anomaly Detection Dataset and a private dataset which is based on thermal-wave and used for non-destructive testing. This technique has been utilized in research for the evaluation of industrial materials. Applying the f-AnoGAN in this domain offers high anomaly detection accuracy.

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Section: Introductionmentioning

confidence: 99%

f-AnoGAN for non-destructive testing in industrial anomaly detection

Sliti

Devanne

Kohler

et al. 2023

Sixteenth International Conference on Quality Control by Artificial Vision

View full text Add to dashboard Cite

show abstract

“…There are two approaches to overcoming data imbalance: the data-level approach, which manipulates data in a balanced way, and the algorithm-level approach, which responds sensitively to class imbalance [9]. Algorithm-level approaches use new variations of existing classification algorithms to solve imbalance problems [10].…”

Section: Introductionmentioning

confidence: 99%

Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique

Eom,

Byeon

2023

Mathematics

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Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; thus, data belonging to the minority class are likely to be misclassified. It is particularly important to overcome this issue when dealing with medical data because class imbalance inevitably arises due to incidence rates within medical datasets. This study adjusted the imbalance ratio (IR) within the National Biobank of Korea dataset “Epidemiologic data of Parkinson’s disease dementia patients” to values of 6.8 (raw data), 9, and 19 and compared four traditional oversampling methods with techniques using the conditional generative adversarial network (CGAN) and conditional tabular generative adversarial network (CTGAN). The results showed that when the classes were balanced with CGAN and CTGAN, they showed a better classification performance than the more traditional oversampling techniques based on the AUC and F1-score. We were able to expand the application scope of GAN, widely used in unstructured data, to structured data. We also offer a better solution for the imbalanced data problem and suggest future research directions.

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Academic Expert Search Method Using Importance and Quality of Papers

Cited by 2 publications

References 4 publications

f-AnoGAN for non-destructive testing in industrial anomaly detection

f-AnoGAN for non-destructive testing in industrial anomaly detection

Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique

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