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dc.contributorPerzynski, Bogdan
dc.contributor.advisorFussell, Donald S.
dc.creatorXie, Angela May
dc.date.accessioned2019-07-23T14:32:14Z
dc.date.available2019-07-23T14:32:14Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/2152/75206
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/2312
dc.description.abstractThe purpose of this thesis was to explore ways of creating computer art using an activation maximization generative adversarial network (AM GAN). GANs are a recent development in machine learning. AM GANs, in particular, use the GAN model to generate images that highly activate a specific neuron within an image recognition neural network. I frame my project in the context of New Aesthetic, an art movement that focuses on the collaboration between humans and digital technology. Since its emergence, New Aesthetic has been criticized for a few different reasons. I wanted to address these criticisms using an AM GAN while also exploring ways that an AM GAN can be controlled.
dc.subjectactivation maximization generative adversarial network
dc.subjectAM GAN
dc.subjectGAN
dc.subjectneuron
dc.subjectcontrol
dc.titleUsing A Generative Adversarial Network To Explore The Newaesthetic


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