• Title, Summary, Keyword: Deep Learning

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Deep Structured Learning: Architectures and Applications

  • Lee, Soowook
    • International Journal of Advanced Culture Technology
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    • v.6 no.4
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    • pp.262-265
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    • 2018
  • Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

A Review of Deep Learning Research

  • Mu, Ruihui;Zeng, Xiaoqin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.4
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    • pp.1738-1764
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    • 2019
  • With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

Deep Learning Research Trend Analysis using Text Mining

  • Lee, Jee Young
    • International Journal of Advanced Culture Technology
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    • v.7 no.4
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    • pp.295-301
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    • 2019
  • Since the third artificial intelligence boom was triggered by deep learning, it has been 10 years. It is time to analyze and discuss the research trends of deep learning for the stable development of AI. In this regard, this study systematically analyzes the trends of research on deep learning over the past 10 years. We collected research literature on deep learning and performed LDA based topic modeling analysis. We analyzed trends by topic over 10 years. We have also identified differences among the major research countries, China, the United States, South Korea, and United Kingdom. The results of this study will provide insights into research direction on deep learning in the future, and provide implications for the stable development strategy of deep learning.

Deep-Learning-Based Molecular Imaging Biomarkers: Toward Data-Driven Theranostics

  • Choi, Hongyoon
    • Progress in Medical Physics
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    • v.30 no.2
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    • pp.39-48
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    • 2019
  • Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

Learning strategies and deep learning (학습전략과 심층학습)

  • Shin, Hong-Im
    • Korean Medical Education Review
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    • v.11 no.1
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    • pp.35-43
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    • 2009
  • Learning strategies are defined as behaviors and thoughts that a learner engages in during learning and that are intended to influence the learner's encoding process. Today, demands for teaching how to learn increase, because there is a lot of complex material which is delivered to students. But learning strategies shouldn't be identified as tricks of students for achieving high scores in exams. Cognitive researchers and theorists assume that learning strategies are related to two types of learning processing, which are described as 'surface learning' and 'deep learning'. In addition learning strategies are associated with learning motivation. Students with 'meaning orientation' who struggle for deep learning, are intrinsically motivated, whereas students with 'reproduction orientation' or 'achieving orientation' are extrinsically motivated. Therefore, to foster active learning and intrinsic motivation of students, it isn't enough to just teach how to learn. Changes of curriculum and assessment methods, that stimulate deep learning and curiosity of students are needed with educators and learners working cooperatively.

Bagging deep convolutional autoencoders trained with a mixture of real data and GAN-generated data

  • Hu, Cong;Wu, Xiao-Jun;Shu, Zhen-Qiu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5427-5445
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    • 2019
  • While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

Analysis of Feature Extraction Algorithms Based on Deep Learning (Deep Learning을 기반으로 한 Feature Extraction 알고리즘의 분석)

  • Kim, Gyung Tae;Lee, Yong Hwan;Kim, Yeong Seop
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.2
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    • pp.60-67
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    • 2020
  • Recently, artificial intelligence related technologies including machine learning are being applied to various fields, and the demand is also increasing. In particular, with the development of AR, VR, and MR technologies related to image processing, the utilization of computer vision based on deep learning has increased. The algorithms for object recognition and detection based on deep learning required for image processing are diversified and advanced. Accordingly, problems that were difficult to solve with the existing methodology were solved more simply and easily by using deep learning. This paper introduces various deep learning-based object recognition and extraction algorithms used to detect and recognize various objects in an image and analyzes the technologies that attract attention.

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A Comparative Study on Performance of Deep Learning Models for Vision-based Concrete Crack Detection according to Model Types (영상기반 콘크리트 균열 탐지 딥러닝 모델의 유형별 성능 비교)

  • Kim, Byunghyun;Kim, Geonsoon;Jin, Soomin;Cho, Soojin
    • Journal of the Korean Society of Safety
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    • v.34 no.6
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    • pp.50-57
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    • 2019
  • In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.

Recent advances in deep learning-based side-channel analysis

  • Jin, Sunghyun;Kim, Suhri;Kim, HeeSeok;Hong, Seokhie
    • ETRI Journal
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    • v.42 no.2
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    • pp.292-304
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    • 2020
  • As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

A Study on Development Deep Learning Based Learning System for Enhancing the Data Analytical Thinking (데이터 분석적 사고력 향상을 위한 딥러닝 기반 학습 시스템 개발 연구)

  • Lee, Young-ho;Koo, Duk-hoi
    • Journal of The Korean Association of Information Education
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    • v.21 no.4
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    • pp.393-401
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    • 2017
  • The purpose of this study is to develop a deep learning based learning system for improving learner's data analytical thinking ability. The contents of the study are as follows. First, deep learning was applied to the discovery learning model to improve data analytical thinking ability. This is a learning method that can generate a model showing the relationship of given data by using the deep learning method, then apply the model to new data to obtain the result. Second, we developed a deep learning based system for DBD learning model. Specifically, we developed a system to generate a model of data using the deep learning method and to apply this model. The research of deep learning based learning system will be a new approach to improve learner's data analytical thinking ability in future society where data becomes more important.