• Title, Summary, Keyword: multi-task learning

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Multi-task Architecture for Singe Image Dynamic Blur Restoration and Motion Estimation (단일 영상 비균일 블러 제거를 위한 다중 학습 구조)

  • Jung, Hyungjoo;Jang, Hyunsung;Ha, Namkoo;Yeon, Yoonmo;Kwon, Ku yong;Sohn, Kwanghoon
    • Journal of Korea Multimedia Society
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    • v.22 no.10
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    • pp.1149-1159
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    • 2019
  • We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

The Effect of Task-Oriented Multi-Sensory Movement Program on Self-efficacy and Writing Ability of Children with ADHD Tendency Accompanied by Learning Delays (과제 중심 다감각운동 프로그램이 학습지연을 동반한 ADHD성향 아동의 자아효능감과 쓰기능력에 미치는 변화)

  • Roh, Heo-Lyun;Kwag, Sung-Won
    • The Journal of Korean society of community based occupational therapy
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    • v.8 no.2
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    • pp.1-14
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    • 2018
  • Objective : The purpose of this study was to investigate the change in self-efficacy and writing ability after applying a Task-Oriented Multi-Sensory Movement Program to children with ADHD tendency accompanied by learning delays. Methods : A Task-Oriented Multi-Sensory Movement Program was implemented to children with ADHD tendency accompanied by learning delays attending S elementary school. The research proceeded in the order of a pre-test, Task-Oriented Multi-Sensory Movement intervention, and a post-test. The first session involved a pre-test, in which the children's self-efficacy and writing ability were examined using self-efficacy test and type 'A' KNISE-BAAT writing test. The multisensory group activity program intervention was conducted for a total of 8 sessions. In the last session, a post-test was conducted using self-efficacy test and type 'B' KNISE-BAAT writing test. Data collected from the tests were analyzed using SPSS Statistics 18. Results : According to the tests taken before and after implementing the Task-Oriented Multi-Sensory Movement Program, there was a significant improvement in self-efficacy (school, society), writing ability(command of vocabulary and sentence). Conclusion : Task-Oriented Multi-Sensory Movement Program may be used as a beneficial measure to improve the self-efficacy and writing abilities of children with ADHD tendency accompanied by learning delays. It is necessary to design various intervention models by combining educational media based on a multisensory approach.

Utilization of age information for speaker verification using multi-task learning deep neural networks (멀티태스크 러닝 심층신경망을 이용한 화자인증에서의 나이 정보 활용)

  • Kim, Ju-ho;Heo, Hee-Soo;Jung, Jee-weon;Shim, Hye-jin;Kim, Seung-Bin;Yu, Ha-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.5
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    • pp.593-600
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    • 2019
  • The similarity in tones between speakers can lower the performance of speaker verification. To improve the performance of speaker verification systems, we propose a multi-task learning technique using deep neural network to learn speaker information and age information. Multi-task learning can improve generalization performances, because it helps deep neural networks to prevent hidden layers from overfitting into one task. However, we found in experiments that learning of age information does not work well in the process of learning the deep neural network. In order to improve the learning, we propose a method to dynamically change the objective function weights of speaker identification and age estimation in the learning process. Results show the equal error rate based on RSR2015 evaluation data set, 6.91 % for the speaker verification system without using age information, 6.77 % using age information only, and 4.73 % using age information when weight change technique was applied.

Multi-modal Emotion Recognition using Semi-supervised Learning and Multiple Neural Networks in the Wild (준 지도학습과 여러 개의 딥 뉴럴 네트워크를 사용한 멀티 모달 기반 감정 인식 알고리즘)

  • Kim, Dae Ha;Song, Byung Cheol
    • Journal of Broadcast Engineering
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    • v.23 no.3
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    • pp.351-360
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    • 2018
  • Human emotion recognition is a research topic that is receiving continuous attention in computer vision and artificial intelligence domains. This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment. The proposed method has the following features. First, the learning performance of the image-based network is greatly improved by employing both multi-task learning and semi-supervised learning using the spatio-temporal characteristic of videos. Second, a model for converting 1-dimensional (1D) landmark information of face into two-dimensional (2D) images, is newly proposed, and a CNN-LSTM network based on the model is proposed for better emotion recognition. Third, based on an observation that audio signals are often very effective for specific emotions, we propose an audio deep learning mechanism robust to the specific emotions. Finally, so-called emotion adaptive fusion is applied to enable synergy of multiple networks. The proposed network improves emotion classification performance by appropriately integrating existing supervised learning and semi-supervised learning networks. In the fifth attempt on the given test set in the EmotiW2017 challenge, the proposed method achieved a classification accuracy of 57.12%.

Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process

  • Lim, Hyunki;Seo, Wangduk;Lee, Jaesung
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.9
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    • pp.7-13
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    • 2018
  • Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

A study on the optimal task-based instructional model: Focused on Korean EFL classroom practice (효율적인 과업중심 교수.학습모형 연구: EFL 교실 상황을 중심으로)

  • Jeon, In-Jae
    • English Language & Literature Teaching
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    • v.11 no.4
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    • pp.365-389
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    • 2005
  • The purpose of this study is to present the task model that is the most effective in English language methodology based on the investigation of task-based performance in Korean EFL classroom practice. The subjects were 538 high school students and 126 high school teachers, each of whom had common experiences using the materials of task-based activities for more than one year. To analyze the data, the program SPSS WIN 11.0 including frequency distribution and chi-square analysis was used. The results of the questionnaire analysis showed that both teachers and students had a comparatively high level of satisfaction in task rationale, but that they had some mixed responses in the fields of input data, settings, and activity types. To conclude, a few suggestions are made to provide some meaningful considerations for the EFL teachers and material developers: a) task goals and rationale that encourage the learner's positive motivation; b) authenticity of input data based on the real-world context; c) collaborative learning environment that enhances communicative interaction; d) proportional representation of the creative problem-solving activities related to discussions and decision-making processes; e) systematic introduction of integrated language skills. It also suggests that the multi-lateral task model, which has some positive assets compared to previous task models, be newly introduced and applied to the second language learning classrooms.

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A light-weight Gender/Age Estimation model based on Multi-taking Deep Learning for an Embedded System (임베디드 시스템을 위한 멀티태스킹 딥러닝 학습 기반 경량화 성별/연령별 추정)

  • Bao, Huy-Tran Quoc;Chung, Sun-Tae
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.483-486
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    • 2020
  • Age estimation and gender classification for human is a classic problem in computer vision. Almost research focus just only one task and the models are too heavy to run on low-cost system. In our research, we aim to apply multitasking learning to perform both task on a lightweight model which can achieve good precision on embedded system in the real time.

X-ray Image Segmentation using Multi-task Learning

  • Park, Sejin;Jeong, Woojin;Moon, Young Shik
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.3
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    • pp.1104-1120
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    • 2020
  • The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

Fast and Robust Face Detection based on CNN in Wild Environment (CNN 기반의 와일드 환경에 강인한 고속 얼굴 검출 방법)

  • Song, Junam;Kim, Hyung-Il;Ro, Yong Man
    • Journal of Korea Multimedia Society
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    • v.19 no.8
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    • pp.1310-1319
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    • 2016
  • Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

Learning soccer robot using genetic programming

  • Wang, Xiaoshu;Sugisaka, Masanori
    • 제어로봇시스템학회:학술대회논문집
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    • pp.292-297
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    • 1999
  • Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.

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