Design of Semantic-Based Colorization of Graphical User Interface Through Conditional Generative Adversarial Nets

References

• Android. (2018). The color system - material design. Retrieved from https://material.io/design/color/the-color-system.html
   Google Scholar
• Bahng, H., Yoo, S., Cho, W., Keetae Park, D., Wu, Z., Ma, X., & Choo, J. (2018). Coloring with words: Guiding image colorization through text-based palette generation. In Proceedings of the european conference on computer vision (eccv) (pp. 431–447). doi:https://doi.org/10.1177/1753193417738166
   Google Scholar
• Chang, H., Fried, O., Liu, Y., DiVerdi, S., & Finkelstein, A. (2015). Palette-based photo recoloring. ACM Transactions on Graphics (TOG), 34(4), 139. doi:https://doi.org/10.1145/2766978
   Google Scholar
• Cho, J., Yun, S., Lee, K., & Choi, J. Y. (2017). Palettenet: Image recolorization with given color palette. In Proceedings of the ieee conference on computer vision and pattern recognition workshops (pp. 62–70). Hawaii, USA
   Google Scholar
• Choi, K., & Suk, H.-J. (2015). Optimal employment of color attributes to achieve saliency in icon matrix designs. Color Research & Application, 40(5), 429–436. doi:https://doi.org/10.1002/col.21922
   Web of Science ®Google Scholar
• Color-hex. (2019). Color hex color codes. Retrieved from https://www.color-hex.com/
   Google Scholar
• Dastidar, D. G. (2014). Top 10 principles of mobile interface layout. Accessed, 7(5), 2014.
   Google Scholar
• GitHub. (2017). Bbc news dataset. Retrieved from https://github.com/InvincibleWu/NLP-AdjectiveEmbedding
   Google Scholar
• Jahanian, A., Keshvari, S., Vishwanathan, S., & Allebach, J. P. (2017). Colors–Messengers of concepts: Visual design mining for learning color semantics. ACM Transactions on Computer-Human Interaction (TOCHI), 24(1), 2. doi:https://doi.org/10.1145/3009924
   Google Scholar
• Karacan, L., Akata, Z., Erdem, A., & Erdem, E. (2016). Learning to generate images of outdoor scenes from attributes and semantic layouts. arXiv preprint arXiv:1612.00215.
   Google Scholar
• Kawakami, K., Dyer, C., Routledge, B. R., & Smith, N. A. (2016). Character sequence models for colorfulwords. arXiv preprint arXiv:1609.08777.
   Google Scholar
• Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
   Google Scholar
• Koo, S. (2016). Automatic colorization with deep convolutional generative adversarial networks. Retrieved from http://cs231n.stanford.edu/reports/2016/pdfs/224 Report.pdf
   Google Scholar
• Lant, K. (2019). Art by computers: How artificial intelligence will shape the future of design. 99designs.
   Google Scholar
• Liu, Y., Cohen, M., Uyttendaele, M., & Rusinkiewicz, S. (2014). Autostyle: Automatic style transfer from image collections to users’ images. In Computer Graphics Forum, 33, 21–31. doi:https://doi.org/10.1111/cgf.2014.33.issue-4
   Web of Science ®Google Scholar
• Mathieu, M., Couprie, C., & LeCun, Y. (2015). Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440.
   Google Scholar
• McMahan, B., & Stone, M. (2015). A bayesian model of grounded color semantics. Transactions of the Association of Computational Linguistics, 3(1), 103–115. doi:https://doi.org/10.1162/tacl_a_00126
   Google Scholar
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, 26, 3111–3119.
   Google Scholar
• Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
   Google Scholar
• Monroe, W., Hawkins, R. X., Goodman, N. D., & Potts, C. (2017). Colors in context: A pragmatic neural model for grounded language understanding. arXiv preprint arXiv:1703.10186.
   Google Scholar
• Nazeri, K., Ng, E., & Ebrahimi, M. (2018). Image colorization using generative adversarial networks. In International conference on articulated motion and deformable objects (pp. 85–94). Palma de Mallorca, Spain
   Google Scholar
• Oulasvirta, A. (2017). User interface design with combinatorial optimization. Computer, 50(1), 40–47. doi:https://doi.org/10.1109/MC.2017.6
   Web of Science ®Google Scholar
• Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (emnlp) (pp. 1532–1543). Doha, Qatar.
   Google Scholar
• Pettersson, R. (2014). Information design theories. Journal of Visual Literacy, 33(1), 1–96. doi:https://doi.org/10.1080/23796529.2014.11674713
   Google Scholar
• Philips, M. (2018). The present and future of ai in design infographic. Toptal.
   Google Scholar
• Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396.
   Google Scholar
• Reed, S. E., Akata, Z., Mohan, S., Tenka, S., Schiele, B., & Lee, H. (2016). Learning what and where to draw. In D.D. Lee,M. Sugiyama and U.V. Luxburg and I. Guyon & R. Garnett.(Eds.), Advances in neural information processing systems (pp. 217–225). USA: Curran Associates Inc.
   Google Scholar
• Suárez, P. L., Sappa, A. D., & Vintimilla, B. X. (2017). Infrared image colorization based on a triplet dcgan architecture. In 2017 ieee conference on computer vision and pattern recognition workshops (cvprw) (pp. 212–217). doi:https://doi.org/10.5125/jkaoms.2017.43.3.212
   Google Scholar
• Tam, K. (2018). Can artificial intelligence replace graphic designers? Medium.
   Google Scholar
• Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014). Learning sentiment-specific word embedding for twitter sentiment classification. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: Long papers) (Vol.1, pp. 1555–1565). Baltimore, Maryland, USA
   Google Scholar
• Teixeira, F. (2017). How ai has started to impact our work as designers. Medium.
   Google Scholar
• van Bodegraven, J. (2019). Brain food!: Ai driven design (Vol. 4). Retrieved from awwwards.books
   Google Scholar
• Vetrov, Y. (2017). Algorithm-driven design: How artificial intelligence is changing design. Smashing magazine. https://www. smashingmagazine. com/2017/01/algorithm-driven-design-howartificial-intelligence-changing-design.
   Google Scholar
• Xiao, Y., Zhou, P., & Zheng, Y. (2018). Interactive deep colorization with simultaneous global and local inputs. arXiv preprint arXiv:1801.09083.
   Google Scholar
• Yoo, D., Kim, N., Park, S., Paek, A. S., & Kweon, I. S. (2016). Pixel-level domain transfer. In European conference on computer vision (pp. 517–532). doi:https://doi.org/10.3389/fnins.2015.00517
   Google Scholar
• Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. N. (2017). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the ieee international conference on computer vision (pp. 5907–5915). Venice, Italy.
   Google Scholar
• Zhang, R., Isola, P., & Efros, A. A. (2016). Colorful image colorization. In European conference on computer vision (pp. 649–666). Amsterdam, Netherlands.
   Google Scholar
• Zhang, R., Zhu, J.-Y., Isola, P., Geng, X., Lin, A. S., Yu, T., & Efros, A. A. (2017). Real-time user-guided image colorization with learned deep priors. arXiv preprint arXiv:1705.02999.
   Google Scholar
• Zhou, Y., & Berg, T. L. (2016). Learning temporal transformations from time-lapse videos. In European conference on computer vision (pp. 262–277). doi:https://doi.org/10.1142/S2424835516720103
   Google Scholar