Stacked capsule autoencoder based generative adversarial network

dc.contributor.advisorFernando KSD
dc.contributor.authorMadhusanka GAC
dc.date.accept2020
dc.date.accessioned2020
dc.date.available2020
dc.date.issued2020
dc.description.abstractConvolutional neural network based generative adversarial networks have become the dominant generative model in the field of generative deep learning. But limitations of convolutional neural networks affect generative adversarial networks also, since most of the current generative adversarial networks are based on convolutional neural networks. The main limitation of convolutional neural networks is that they are invariant. In other words, convolutional neural networks can’t preserve spatial information of features in an image. In contrast, capsule networks gained attention in recent years due to their equivariant architecture which preserves spatial information. Stacked capsule autoencoder is a type of capsule networks that is able to overcome the limitations that convolutional neural networks suffer from. Stacked capsule autoencoder is an equivariant model which preserves spatial, relational, geometrical information between parts and objects in an image. So in this research we implemented a generative adversarial network which uses stacked capsule autoencoder as the discriminator of it, by replacing the conventional convolutional neural network discriminator. Then we evaluated the implementation of stacked capsule autoencoder based generative adversarial network using MNIST images. As the qualitative evaluation we observed the visual quality of generated images. Quality and diversity of the generated images are acceptable. Then we evaluated our model quantitatively using inception score for MNIST. Findings of this research show that, the stacked capsule autoencoder can be used as the discriminator of a generative adversarial network instead a convolutional neural network and its performances are plausible.en_US
dc.identifier.accnoTH4835en_US
dc.identifier.citationMadhusanka, G.A.C. (2020). Stacked capsule autoencoder based generative adversarial network [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21205
dc.identifier.degreeMSc in Artificial Intelligenceen_US
dc.identifier.departmentDepartment of Computational Mathematicsen_US
dc.identifier.facultyITen_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/21205
dc.language.isoenen_US
dc.subjectSTACKED CAPSULE AUTO-ENCODERen_US
dc.subjectGENERATIVE ADVERSARIAL NETWORKen_US
dc.subjectINFORMATION TECHNOLOGY -Dissertationen_US
dc.subjectCOMPUTATIONAL MATHEMATICS -Dissertationen_US
dc.subjectARTIFICIAL INTELLIGENCE -Dissertationen_US
dc.titleStacked capsule autoencoder based generative adversarial networken_US
dc.typeThesis-Full-texten_US

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