一种渐进式增长条件生成对抗网络模型

马辉; 王瑞琴; 杨帅

doi:10.11959/j.issn.1000-0801.2023134

您当前的位置：

首页 >

文章列表页 >

一种渐进式增长条件生成对抗网络模型

研究与开发 | 更新时间：2024-06-05

- 一种渐进式增长条件生成对抗网络模型
- A progressive growing of conditional generative adversarial networks model
- 电信科学 2023年39卷第6期页码：105-113
- 作者机构：
- 作者简介：
  
  [ "马辉（1997- ），男，湖州师范学院硕士生，主要研究方向为生成对抗网络、计算机视觉" ]
  [ "王瑞琴（1979- ），女，博士，湖州师范学院教授，主要研究方向为机器学习与数据挖掘、社交网络分析、个性化推荐" ]
  [ "杨帅（1996- ），男，湖州师范学院硕士生，主要研究方向为图神经网络、推荐系统" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62277016)
- DOI：10.11959/j.issn.1000-0801.2023134
  中图分类号： TP391
- 网络出版日期：2023-06，
  
  纸质出版日期：2023-06-20
- 稿件说明：
移动端阅览
马辉, 王瑞琴, 杨帅. 一种渐进式增长条件生成对抗网络模型[J]. 电信科学, 2023,39(6):105-113.

Hui MA, Ruiqin WANG, Shuai YANG. A progressive growing of conditional generative adversarial networks model[J]. Telecommunications science, 2023, 39(6): 105-113.
马辉, 王瑞琴, 杨帅. 一种渐进式增长条件生成对抗网络模型[J]. 电信科学, 2023,39(6):105-113. DOI： 10.11959/j.issn.1000-0801.2023134.

Hui MA, Ruiqin WANG, Shuai YANG. A progressive growing of conditional generative adversarial networks model[J]. Telecommunications science, 2023, 39(6): 105-113. DOI： 10.11959/j.issn.1000-0801.2023134.

摘要

渐进式增长生成对抗网络（PGGAN）是一种能够生成高分辨图像的网络模型，但是当样本间的类别不平衡或者样本类别过于相似或不相似时，容易出现模式崩溃现象而导致生成效果不佳。提出一种渐进式增长条件生成对抗网络（PGCGAN）模型，将条件生成对抗网络的思想引入PGGAN，在PGGAN的基础上加入类别信息作为条件，在网络结构和小批量标准差两个方面对 PGGAN 进行了改进，缓解图像生成过程中的模式崩溃现象。在对3个数据集的实验中，相比于PGGAN，PGCGAN在起始分数（IS）和Fréchet距离（FID）两个评价图像生成的指标方面都有较大程度的提升，生成的图像具有更高的多样性和真实性；且PGCGAN可以同时训练多个无关联的数据集而不崩溃，在类别不平衡或数据过于相似和不相似的数据集中均能产生高质量的图像。

Abstract

Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However

when the categories of samples are unbalanced

or the categories of samples are too similar or too dissimilar

it is prone to produce mode collapse

resulting in poor image generation effect.A progressive growing of conditional generative adversarial networks (PGCGAN) model was proposed.The idea of conditional generative adversarial networks (CGAN) was introduced into PGGAN.Using category information as condition

PGGAN was improved in two aspects of network structure and mini-batch standard deviation

and the phenomenon of model collapse in the process of image generation was alleviated.In the experiments on the three data sets

compared with PGGAN

PGCGAN has a greater degree of improvement in inception score and Fréchet inception distance

two evaluation indicators for image generation

and the generated images have higher diversity and authenticity; and PGCGAN multiple unrelated datasets can be trained simultaneously without crashing

and high-quality images can be produced in datasets with imbalanced categories or data that are too similar and dissimilar.

关键词

Keywords

references

HARSHVARDHAN G M , GOURISARIA M K , PANDEY M , et al . A comprehensive survey and analysis of generative models in machine learning [J ] . Computer Science Review , 2020 ( 38 ): 100285 .

OUSSIDI A , ELHASSOUNY A . Deep generative models:survey [C ] // Proceedings of 2018 International Conference on Intelligent Systems and Computer Vision (ISCV) . Piscataway:IEEE Press , 2018 : 1 - 8 .

SHAHAM T R , DEKEL T , MICHAELI T . SinGAN:learning a generative model from a single natural image [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2019 : 4569 - 4579 .

于海涛 , 杨小汕 , 徐常胜 . 基于多模态输入的对抗式视频生成方法 [J ] . 计算机研究与发展 , 2020 , 57 ( 7 ): 1522 - 1530 .

YU H T , YANG X S , XU C S . Antagonistic video generation method based on multimodal input [J ] . Journal of Computer Research and Development , 2020 , 57 ( 7 ): 1522 - 1530 .

ZHU J Y , PARK T , ISOLA P , et al . Unpaired image-to-image translation using cycle-consistent adversarial networks [C ] // Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2017 : 2242 - 2251 .

YUAN W , NEUBIG G , LIU P . BARTScore:evaluating generated text as text generation [J ] . Advances in Neural Information Processing Systems , 2021 ( 34 ): 27263 - 27277 .

LIAO J W , SHI Y , GONG M , et al . Generating human readable transcript for automatic speech recognition with pre-trained language model [C ] // Proceedings of ICASSP 2021 - 2021 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP) . Piscataway:IEEE Press , 2021 : 7578 - 7582 .

陈华华 , 陈哲 . 基于钉板分布稀疏变分自编码器的异常检测算法研究 [J ] . 电信科学 , 2022 , 38 ( 12 ): 65 - 77 .

CHEN H H , CHEN Z . Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior [J ] . Telecommunications Science , 2022 , 38 ( 12 ): 65 - 77 .

JIAO P , GUO X , JING X , et al . Temporal network embedding for link prediction via VAE joint attention mechanism [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 33 ( 12 ): 7400 - 7413 .

HUANG D , ZHU X , LI R , et al . Feature screening for network autoregression model [J ] . Statistica Sinica , 2021 ( 31 ): 1239 .

AGGARWAL A , MITTAL M , BATTINENI G . Generative adversarial network:an overview of theory and applications [J ] . International Journal of Information Management Data Insights , 2021 , 1 ( 1 ): 100004 .

宋珂慧 , 张莹 , 张江伟 , 等 . 基于生成式对抗网络的结构化数据表生成模型 [J ] . 计算机研究与发展 , 2019 , 56 ( 9 ): 1832 - 1842 .

SONG K H , ZHANG Y , ZHANG J W , et al . A generative model for synthesizing structured datasets based on GAN [J ] . Journal of Computer Research and Development , 2019 , 56 ( 9 ): 1832 - 1842 .

MAO X D , LI Q , XIE H R , et al . Least squares generative adversarial networks [C ] // Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2017 : 2813 - 2821 .

ARJOVSKY M , BOTTOU L . Towards principled methods for training generative adversarial networks [J ] . Stat , 2017 :1050.

ADLER J , LUNZ S . Banach Wasserstein GAN [J ] . Advances in Neural Information Processing Systems , 2018 ( 31 ).

GULRAJANI I , AHMED F , ARJOVSKY M , et al . Improved training of wasserstein GANs [J ] . Advances in Neural Information Processing Systems , 2017 ( 30 ).

WU J Q , HUANG Z W , THOMA J , et al . Wasserstein divergence for GANs [C ] // Proceedings of European Conference on Computer Vision . Cham:Springer , 2018 : 673 - 688 .

RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks [J ] . arXiv preprint , 2015 ,arXiv:1511.06434.

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J ] . Communications of the ACM , 2017 , 60 ( 6 ): 84 - 90 .

MIRZA M , OSINDERO S . Conditional generative adversarial nets [J ] . Computer Science , 2014 : 2672 - 2680 .

CHEN X , DUAN Y , HOUTHOOFT R , et al . InfoGAN:interpretable representation learning by information maximizing generative adversarial nets [C ] // Proceedings of the 30th International Conference on Neural Information Processing Systems . New York:ACM Press , 2016 : 2180 - 2188 .

ODENA A , OLAH C , SHLENS J . Conditional image synthesis with auxiliary classifier GANs [C ] // Proceedings of the 34th International Conference on Machine Learning - Volume 70 . New York:ACM Press , 2017 : 2642 - 2651 .

LUCIC M , TSCHANNEN M , RITTER M , et al . High-fidelity image generation with fewer labels [J ] . arXiv preprint , 2019 ,arXiv:1903.02271.

LIU S , WANG T Z , BAU D , et al . Diverse image generation via self-conditioned GANs [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Piscataway:IEEE Press , 2020 : 14286 - 14295 .

ZHANG X , CHENG Z , ZHANG X , et al . Posterior promoted GAN with distribution discriminator for unsupervised image synthesis [C ] // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2021 : 6519 - 6528 .

KARRAS T , AILA , LAINE S , et al . Progressive growing of GANs for improved quality,stability,and variation [J ] . arXiv preprint , 2017 ,arXiv:1710.10196.

SALIMANS T , GOODFELLOW I , ZAREMBA W , et al . Improved techniques for training GANs [C ] // Proceedings of the 30th International Conference on Neural Information Processing Systems . New York:ACM Press , 2016 : 2234 - 2242 .

ODENA A , DUMOULIN V , OLAH C . Deconvolution and checkerboard artifacts [J ] . Distill , 2016 , 1 ( 10 ): e3 .

GLOROT X , BENGIO Y . Understanding the difficulty of training deep feedforward neural networks [C ] // Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics.JMLR Workshop and Conference Proceedings . 2010 : 249 - 256 .

HUGHES D P , SALATHE M . An open access repository of images on plant health to enable the development of mobile disease diagnostics [J ] . Computer Science , 2015 ,arXiv:1511.08060.

CHOI Y , UH Y , YOO J , et al . StarGAN v2:diverse image synthesis for multiple domains [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 8188 - 8197 .

CHONG M J , FORSYTH D . Effectively unbiased FID and inception score and where to find them [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 6069 - 6078 .

HEUSEL M , RAMSAUER H , UNTERTHINER T , et al . GANs trained by a two time-scale update rule converge to a local Nash equilibrium [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 : 6629 - 6640 .

浏览量

185

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于时间卷积网络的无监督入侵检测模型

基于生成对抗网络的超宽带数字信道建模

基于多文本描述的图像生成方法

信号增强网络驱动的调制识别

基于掩模提取的SAR图像对抗样本生成方法