Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning
المؤلفون المشاركون
Yang, Wenjia
Bi, Fangming
Man, Zijian
Xia, Yang
Liu, Wei
Fu, Xuanyi
Gao, Lei
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-13
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Generative adversarial networks are currently used to solve various problems and are one of the most popular models.
Generator and discriminator are characteristics of continuous game process in training.
While improving the quality of generated pictures, it will also make it difficult for the loss function to be stable, and the training speed will be extremely slow compared with other methods.
In addition, since the generative adversarial networks directly learns the data distribution of samples, the model will become uncontrollable and the freedom of the model will become too large when the original data distribution is constantly approximated.
A new transfer learning training idea for the unsupervised generation model is proposed based on the generation network.
The decoder of trained variational autoencoders is used as the network architecture and parameters to generative adversarial network generator.
In addition, the standard normal distribution is obtained by sampling and then input into the model to control the degree of freedom of the model.
Finally, we evaluated our method on using the MNIST, CIFAR10, and LSUN datasets.
The experiment shows that our proposed method can make the loss function converge as quickly as possible and increase the model accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bi, Fangming& Man, Zijian& Xia, Yang& Liu, Wei& Yang, Wenjia& Fu, Xuanyi…[et al.]. 2020. Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202242
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bi, Fangming…[et al.]. Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1202242
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bi, Fangming& Man, Zijian& Xia, Yang& Liu, Wei& Yang, Wenjia& Fu, Xuanyi…[et al.]. Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202242
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1202242
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر