Generative adversarial network: An overview

被引：0
作者：
Luo J. [1 ]
Huang J. [1 ]
机构：
[1] School of Mechanical Engineering, North University of China, Taiyuan
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2019年 / 40卷 / 03期
关键词：
Adversarial training; Deep learning; Generative adversarial network; Machine learning; Unsupervised learning;
D O I：
10.19650/j.cnki.cjsi.J1804413
中图分类号：
学科分类号：
摘要：
Generative adversarial network(GAN) is an active branch of deep learning field, which has become a popular research direction in the field of artificial intelligence. GAN adopts an unsupervised learning method and automatically learns from the source data, which can produce amazing effects without artificially labeling data. In this paper, we present the background, basic idea of GAN and comb its related theory, training mechanism and state-of-the-art applications. We also summarize the common network architectures, training skills and model evaluation metrics, and compareGAN with other generative model VAE and GAN variants. Finally, we point out the advantages and disadvantages of the GAN and look forward to the further development direction. © 2019, Science Press. All right reserved.
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收藏
页码：74 / 84
页数：10
相关论文
共 76 条
  • [1] Dai J., Li Y., He K., Et al., R-FCN: Object detection via region-basedfully convolutional networks, The Advances in Neural Information Processing Systems, pp. 379-387, (2016)
  • [2] Hong S., Roh B., Kim K.H., Et al., PVANet: Lightweight deep neural networks for real-time object detection, Computer Vision and Pattern Recognition, (2016)
  • [3] Oord A.V.D., Dieleman S., Zen H., Et al., WaveNet: A generative model for raw audio, Computer Science for Sound, (2016)
  • [4] Li X., Qin T., Yang J., Et al., LightRNN: Memory and computation-efficient recurrent neural networks, Computation and Language, (2016)
  • [5] Dauphin Y.N., Fan A., Auli M., Et al., Language modeling with gated convolutional networks, Computation and Language, (2016)
  • [6] Sai D., White paper on core industries of Artificial Intelligencedevelopment
  • [7] Diederik P.K., Max W., Auto-encoding variational Bayes, Statistics for Machine Learning, (2013)
  • [8] Goodfellow I.J., Pouget-Abadie J., Mirza M., Et al., Generative adversarial nets, International Conference on Neural Information Processing Systems, pp. 2672-2680, (2014)
  • [9] Zen G., Sangineto E., Ricci E., Et al., Unsupervised domain adaptationfor personalized facial emotion recognition, International Conference on Multimodal Interaction, pp. 128-135, (2014)
  • [10] Fanny, Tjeng W.C., Deep learning for imbalance data classification using class expert generative adversarial network, Computer Science for Machine Learning, (2018)
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