Computer Science ›› 2021, Vol. 48 ›› Issue (1): 241-246.doi: 10.11896/jsjkx.200700187

• Artificial Intelligence • Previous Articles     Next Articles

Conditional Generative Adversarial Network Based on Self-attention Mechanism

YU Wen-jia, DING Shi-fei   

  1. School of Computer Science and Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
  • Received:2020-07-29 Revised:2020-09-22 Online:2021-01-15 Published:2021-01-15
  • About author:YU Wen-jia,born in 1994,postgradua-te,is a student member of China Computer Federation.His main research interests include deep learning and computer vision.
    DING Shi-fei,born in 1963,Ph.D,professor,Ph.D supervisor,is a director of China Computer Federation.His main research interests include artificial intelligence,machine learning,pattern recognition and data mining.
  • Supported by:
    National Natural Science Foundation of China (61672522,61976216).

Abstract: In recent years,more and more generative adversarial networks appear in various fields of deep learning.Conditional generative adversarial networks(cGAN) are the first to introduce supervised learning into unsupervised GANs,which makes it possible for adversarial networks to generate labeled data.Traditional GAN generates images through multiple convolution operations to simulate the dependency among different regions.However,cGAN only improves the objective function of GAN,but does not change its network structure.Therefore,the problem also exists in cGAN that when the distance between features in thegene-rated image is long,features have relatively less relationship,resulting in unclear details of the generated image.In order to solve this problem,this paper introduces Self-attention mechanism to cGAN and proposes a new model named SA-cGAN.The model generates consistent objects or scenes by using features in the long distance of the image,so that the generative ability of conditional GAN is improved.SA-cGAN is experimented on the CelebA and MNIST handwritten datasets and compared with several commonly used generative models such as DCGAN,cGAN.Results prove that the proposed model has made some progress in the field of image generation.

Key words: cGAN, Deep learning, Generative adversarial network, SA-cGAN, Self-attention

CLC Number: 

  • TP391
[1] GOODFELLOW I J,POUGET A J,MIRZA M,et al.Generative Adversarial Nets[J].arXiv:1406.2661.
[2] CAO Y J,JIA L L,CHEN Y X,et al.Review of computer vision based on generative adversarial networks[J].Journal of Image and Graphics,2018,23(10):1433-1449.
[3] WANG K F,GOU C,DUAN Y J,et al.Generative Adversarial Networks:The State of the Art and Beyond[J].ACTA Automatica Sinica,2017,43(3):321-332.
[4] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436.
[5] JÜRGEN S.Deep learning in neural networks:An overview[J].Neural Netw,2015,61:85-117.
[6] CHENG J,WANG P S,LI G,et al.Recent advances in efficient computation of deep convolutional neural networks[J].Frontiers of Information Technology & Electronic Engineering,2018,19(1):67-80.
[7] KOZIARSKI M,CYGANEK B.Impact of Low Resolution on Image Recognition with Deep Neural Networks:An Experimental Study[J].International Journal of Applied Mathematics and Computer Science,2018,28(4):735-744.
[8] RADFORD A,METZ L,CHINTALA S.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J].arXiv:1511.06434v2,2016.
[9] MIRZA M,OSINDERO S.Conditional Generative AdversarialNets[J].arXiv:Learning,2014.
[10] ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein GAN[J].arXiv:1701.07875v3,2017.
[11] FUGLEDE B,TOPSOE F.Jensen-Shannon divergence and Hilbert space embedding[C]//International Symposium on Information Theory.IEEE,2004:31.
[12] LU B,HANCOCK E R.Graph Kernels from the Jensen-Shannon Divergence[J].Journal of Mathematical Imaging and Vision,2013,47(1):60-69.
[13] GULRAJANI I,AHMED F,ARJOVSKY M,et al.ImprovedTraining of Wasserstein GANs[J].arXiv:1704.00028v3,2017.
[14] LAWRENCE S,GILES C L,TSOI A C,et al.Face recognition:a convolutional neural-network approach[J].IEEE Transactions on Neural Networks,1997,8(1):98-113.
[15] VRHEL M,SABER E,TRUSSELL H J.Color image generation and display technologies[J].IEEE Signal Processing Magazine,2005,22(1):23-33.
[16] BODLA N,GANG H,CHELLAPPA R.Semi-supervisedFusedGAN for Conditional Image Generation[C]//Computer Vision and Pattern Recognition.2018:669-683.
[17] STEFAN D,RUSSO R,DAVID M,et al.Disjunction Category Labels[C]//Nordic Conference on Information Security Technology for Applications.Springer-Verlag,2011.
[18] GOLDSTONE R L,LIPPA Y,SHIFFRIN R M.Altering object representations through category learning[J].Cognition,2001,78(1):27-43.
[19] ZHANG N,DING S F,ZHANG J.Multi Layer ELM-RBF for Multi-Label Learning[J].Applied Soft Computing,2016,43(6):535-545.
[20] STOCKMAN,GEORGE C.Computer vision[M].PrenticeHall,2001.
[21] CAO K,WU,LUO L Z,et al.Face completion algorithm based on condition generation adversarial network[J].Transducer and Microsystem Technologie,2019,38(6):129-132.
[22] TANG X L,DU Y M,LIU Y W,et al.Image Recognition With Conditional Deep Convolutional Generative Adversarial Networks[J].ACTA Automatica Sinica,2018,44(5):855-864.
[23] ZHANG H,GOODFELLOW I,METAXAS D,et al.Self-Attention Generative Adversarial Networks[J].arXiv:1805.08318v2,2019.
[24] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is All you Need[C]//Neural Information Processing Systems.2017:5998-6008.
[25] LU J J,GONG Y.Text sentiment classification model based on self-attention and expanded convolutional neural network[J].Computer Engineering and Design,2020,41(6):1645-1651.
[26] COLLOBERT R,WESTON J,BOTTOU L,et al.Natural Language Processing (Almost) from Scratch[J].Journal of Machine Learning Research,2011,12:2493-2537.
[27] LIU Z W,LUO P,WANG X G,et al.Large-scale celebfaces attributes (celeba) dataset[J].Retrieved August,2018,15.
[28] LI D.The MNIST Database of Handwritten Digit Images for Machine Learning Research[J].IEEE Signal Processing Magazine,2012,29(6):141-142.
[29] KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[J].arXiv:1412.6980v9,2014.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220.
[3] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[4] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[5] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[6] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[7] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[8] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[9] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[10] CHEN Kun-feng, PAN Zhi-song, WANG Jia-bao, SHI Lei, ZHANG Jin. Moderate Clothes-Changing Person Re-identification Based on Bionics of Binocular Summation [J]. Computer Science, 2022, 49(8): 165-171.
[11] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[12] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[13] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[14] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[15] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!