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Neural Information Processing

26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part I

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Tom Gedeon

    1. Australian National University, Canberra, Australia

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  2. Kok Wai Wong

    1. Murdoch University, Murdoch, Australia

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  3. Minho Lee

    1. Kyungpook National University, Daegu, Korea (Republic of)

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11953)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ICONIP 2019.

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Table of contents (63 papers)

  1. Front Matter

    Pages i-xxii
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  2. Adversarial Networks and Learning

    1. Front Matter

      Pages 1-1
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    2. FH-GAN: Face Hallucination and Recognition Using Generative Adversarial Network

      • Bayram Bayramli, Usman Ali, Te Qi, Hongtao Lu
      Pages 3-15

    3. Adversarial Learning for Cross-Modal Retrieval with Wasserstein Distance

      • Qingrong Cheng, Youcai Zhang, Xiaodong Gu
      Pages 16-29

    4. Reducing the Subject Variability of EEG Signals with Adversarial Domain Generalization

      • Bo-Qun Ma, He Li, Wei-Long Zheng, Bao-Liang Lu
      Pages 30-42

    5. Multi-view Image Generation by Cycle CVAE-GAN Networks

      • Zhichen Lai, Chenwei Tang, Jiancheng Lv
      Pages 43-54

    6. B-DCGAN: Evaluation of Binarized DCGAN for FPGA

      • Hideo Terada, Hayaru Shouno
      Pages 55-64

    7. A Natural Scene Text Extraction Approach Based on Generative Adversarial Learning

      • Huali Xu, Xiangdong Su, Tongyang Liu, Pengcheng Guo, Guanglai Gao, Feilong Bao
      Pages 65-73

    8. Weakly Supervised Fine-Grained Visual Recognition via Adversarial Complementary Attentions and Hierarchical Bilinear Pooling

      • Xiaofei Li, Jianming Liu, Mingwen Wang
      Pages 74-85

    9. Learning an Adversarial Network for Speech Enhancement Under Extremely Low Signal-to-Noise Ratio Condition

      • Xiangdong Su, Xiang Hao, Zhiyu Wang, Yun Liu, Huali Xu, Tongyang Liu et al.
      Pages 86-97

    10. HaGAN: Hierarchical Attentive Adversarial Learning for Task-Oriented Dialogue System

      • Ting Fang, Tingting Qiao, Duanqing Xu
      Pages 98-109

    11. Learning to Generate Ambiguous Sequences

      • David Iclanzan, László Szilágyi
      Pages 110-121

  3. Convolutional Neural Networks

    1. Front Matter

      Pages 123-123
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    2. Deep Learning for Combo Object Detection

      • Jing Zhao, Iman Tabatabaei Ardekani, Shaoning Pang
      Pages 125-137

    3. DasNet: Dynamic Adaptive Structure for Accelerating Multi-task Convolutional Neural Network

      • Yinan Ma, Jing Wu, Chengnian Long
      Pages 138-150

    4. Confusion-Aware Convolutional Neural Network for Image Classification

      • Liguang Yan, Baojiang Zhong, Kai-Kuang Ma
      Pages 151-161

    5. Feature Learning and Data Compression of Biosignals Using Convolutional Autoencoders for Sleep Apnea Detection

      • Rim Haidar, Irena Koprinska, Bryn Jeffries
      Pages 162-174

    6. Self-Adaptive Network Pruning

      • Jinting Chen, Zhaocheng Zhu, Cheng Li, Yuming Zhao
      Pages 175-186

    7. Text-Augmented Knowledge Representation Learning Based on Convolutional Network

      • Chunfeng Liu, Yan Zhang, Mei Yu, Ruiguo Yu, Xuewei Li, Mankun Zhao et al.
      Pages 187-198

    8. A Novel Online Ensemble Convolutional Neural Networks for Streaming Data

      • Xuan Cuong Pham, Thi Thu Thuy Nguyen, Alan Wee-Chung Liew
      Pages 199-210
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Other volumes

  1. Neural Information Processing

  2. Neural Information Processing

  3. Neural Information Processing

  4. Neural Information Processing

  5. Neural Information Processing

Keywords

About this book

The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019.

The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 11953, is organized in topical sections on adversarial networks and learning; convolutional neural networks; deep neural networks; feature learning and representation; human centred computing; human centred computing and medicine; hybrid models; and artificial intelligence and cybersecurity.


Editors and Affiliations

  • Australian National University, Canberra, Australia

    Tom Gedeon

  • Murdoch University, Murdoch, Australia

    Kok Wai Wong

  • Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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