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pro vyhledávání: '"Atici, Efehan"'
Recently, the discovery of interpretable directions in the latent spaces of pre-trained GANs has become a popular topic. While existing works mostly consider directions for semantic image manipulations, we focus on an abstract property: creativity. C
Externí odkaz:
http://arxiv.org/abs/2112.06978
Autor:
Gokay, Dilara, Simsar, Enis, Atici, Efehan, Ahmetoglu, Alper, Yuksel, Atif Emre, Yanardag, Pinar
In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder (http://artbreeder.com), where users interpolate multiple GAN-generated ima
Externí odkaz:
http://arxiv.org/abs/2108.09752
Publikováno v:
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); 2016, p1578-1583, 6p