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Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning
المؤلفون المشاركون
Hah, Junghoon
Lee, Woojin
Lee, Jaewook
Park, Saerom
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-17
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
This paper describes a new image generation algorithm based on generative adversarial network.
With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the input images.
Our model not only adversarially minimizes the Wasserstein distance-based losses of the discriminator and generator but also maximizes the mutual information between small subset of the latent variables and the observation.
We also train our model with proportional control theory to keep the equilibrium between the discriminator and the generator balanced, and as a result, our generative adversarial network can mitigate the convergence problem.
Through the experiments on real images, we validate our proposed method, which can manipulate the generated images as desired by controlling the latent codes of input variables.
In addition, the visual qualities of produced images are effectively maintained, and the model can stably converge to the equilibrium.
However, our model has a difficulty in learning disentangling factors because our model does not regularize the independence between the interpretable factors.
Therefore, in the future, we will develop a generative model that can learn disentangling factors.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hah, Junghoon& Lee, Woojin& Lee, Jaewook& Park, Saerom. 2018. Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1130807
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hah, Junghoon…[et al.]. Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1130807
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hah, Junghoon& Lee, Woojin& Lee, Jaewook& Park, Saerom. Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1130807
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130807
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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