• Title, Summary, Keyword: Data Augmentation

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Data Augmentation for DNN-based Speech Enhancement (딥 뉴럴 네트워크 기반의 음성 향상을 위한 데이터 증강)

  • Lee, Seung Gwan;Lee, Sangmin
    • Journal of Korea Multimedia Society
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    • v.22 no.7
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    • pp.749-758
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    • 2019
  • This paper proposes a data augmentation algorithm to improve the performance of DNN(Deep Neural Network) based speech enhancement. Many deep learning models are exploring algorithms to maximize the performance in limited amount of data. The most commonly used algorithm is the data augmentation which is the technique artificially increases the amount of data. For the effective data augmentation algorithm, we used a formant enhancement method that assign the different weights to the formant frequencies. The DNN model which is trained using the proposed data augmentation algorithm was evaluated in various noise environments. The speech enhancement performance of the DNN model with the proposed data augmentation algorithm was compared with the algorithms which are the DNN model with the conventional data augmentation and without the data augmentation. As a result, the proposed data augmentation algorithm showed the higher speech enhancement performance than the other algorithms.

A Classification Method Using Data Reduction

  • Uhm, Daiho;Jun, Sung-Hae;Lee, Seung-Joo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.1
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    • pp.1-5
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    • 2012
  • Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

Validation Data Augmentation for Improving the Grading Accuracy of Diabetic Macular Edema using Deep Learning (딥러닝을 이용한 당뇨성황반부종 등급 분류의 정확도 개선을 위한 검증 데이터 증강 기법)

  • Lee, Tae Soo
    • Journal of Biomedical Engineering Research
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    • v.40 no.2
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    • pp.48-54
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    • 2019
  • This paper proposed a method of validation data augmentation for improving the grading accuracy of diabetic macular edema (DME) using deep learning. The data augmentation technique is basically applied in order to secure diversity of data by transforming one image to several images through random translation, rotation, scaling and reflection in preparation of input data of the deep neural network (DNN). In this paper, we apply this technique in the validation process of the trained DNN, and improve the grading accuracy by combining the classification results of the augmented images. To verify the effectiveness, 1,200 retinal images of Messidor dataset was divided into training and validation data at the ratio 7:3. By applying random augmentation to 359 validation data, $1.61{\pm}0.55%$ accuracy improvement was achieved in the case of six times augmentation (N=6). This simple method has shown that the accuracy can be improved in the N range from 2 to 6 with the correlation coefficient of 0.5667. Therefore, it is expected to help improve the diagnostic accuracy of DME with the grading information provided by the proposed DNN.

Performance Analysis of Data Augmentation for Surface Defects Detection (표면 결함 검출을 위한 데이터 확장 및 성능분석)

  • Kim, Junbong;Seo, Kisung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.5
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    • pp.669-674
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    • 2018
  • Data augmentation is an efficient way to reduce overfitting on models and to improve a performance supplementing extra data for training. It is more important in deep learning based industrial machine vision. Because deep learning requires huge scale of learning data to learn a model, but acquisition of data can be limited in most of industrial applications. A very generic method for augmenting image data is to perform geometric transformations, such as cropping, rotating, translating and adjusting brightness of the image. The effectiveness of data augmentation in image classification has been reported, but it is rare in defect inspections. We explore and compare various basic augmenting operations for the metal surface defects. The experiments were executed for various types of defects and different CNN networks and analysed for performance improvements by the data augmentations.

Data Augmentation Method of Small Dataset for Object Detection and Classification (영상 내 물체 검출 및 분류를 위한 소규모 데이터 확장 기법)

  • Kim, Jin Yong;Kim, Eun Kyeong;Kim, Sungshin
    • The Journal of Korea Robotics Society
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    • v.15 no.2
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    • pp.184-189
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    • 2020
  • This paper is a study on data augmentation for small dataset by using deep learning. In case of training a deep learning model for recognition and classification of non-mainstream objects, there is a limit to obtaining a large amount of training data. Therefore, this paper proposes a data augmentation method using perspective transform and image synthesis. In addition, it is necessary to save the object area for all training data to detect the object area. Thus, we devised a way to augment the data and save object regions at the same time. To verify the performance of the augmented data using the proposed method, an experiment was conducted to compare classification accuracy with the augmented data by the traditional method, and transfer learning was used in model learning. As experimental results, the model trained using the proposed method showed higher accuracy than the model trained using the traditional method.

Experimental Analysis of Equilibrization in Binary Classification for Non-Image Imbalanced Data Using Wasserstein GAN

  • Wang, Zhi-Yong;Kang, Dae-Ki
    • International Journal of Internet, Broadcasting and Communication
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    • v.11 no.4
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    • pp.37-42
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    • 2019
  • In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

SBAS Non-Standard Data Transmission Method for Korea Augmentation Satellite System Applications (KASS 활용을 위한 위성기반 보강항법시스템(SBAS) 비규격 데이터 전송 방법 연구)

  • Park, Jae-ik;Lee, Eunsung;Heo, Moon-beom;Nam, Gi-wook
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.12
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    • pp.1861-1867
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    • 2016
  • Korea augmentation satellite system (KASS), which is a satellite-based augmentation system tailored for Korea, was launched for development in 2014. SBAS is a standard for aviation but it can also be utilized in non-aviation applications. The type and content of transmitted in SBAS data format are restricted. In order to utilize SBAS in fields that require the precision within centimeters, additional information has to be transmitted. It is important that data transmitted in nonstandard SBAS data not affect any operation of SBAS equipment. In this paper, we propose a non-standard SBAS data transmission method applicable to non-aviation applications that does not affect aviation SBAS receivers.

Synthetic Data Augmentation for Plant Disease Image Generation using GAN (GAN을 이용한 식물 병해 이미지 합성 데이터 증강)

  • Nazki, Haseeb;Lee, Jaehwan;Yoon, Sook;Park, Dong Sun
    • Proceedings of the Korea Contents Association Conference
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    • pp.459-460
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    • 2018
  • In this paper, we present a data augmentation method that generates synthetic plant disease images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation techniques to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of 2789 images of tomato plant diseases (Gray mold, Canker, Leaf mold, Plague, Leaf miner, Whitefly etc.).

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Bayesian Estimation for Skew Normal Distributions Using Data Augmentation

  • Kim Hea-Jung
    • Communications for Statistical Applications and Methods
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    • v.12 no.2
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    • pp.323-333
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    • 2005
  • In this paper, we develop a MCMC method for estimating the skew normal distributions. The method utilizing the data augmentation technique gives a simple way of inferring the distribution where fully parametric frequentist approaches are not available for small to moderate sample cases. Necessary theories involved in the method and computation are provided. Two numerical examples are given to demonstrate the performance of the method.