Method for Exploring Generative Adversarial Networks (GANs) via Automatically Generated Image Galleries | Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (2024)

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Amazon. 2020. Amazon Mechanical Turk. https://www.mturk.com/

Christophe Andrieu, Nando DeFreitas, Arnaud Doucet, and MichaelI Jordan. 2003. An introduction to MCMC for machine learning. Machine learning 50, 1-2 (2003), 5–43.

David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, JoshuaB. Tenenbaum, WilliamT. Freeman, and Antonio Torralba. 2019. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR).

Ali Borji. 2019. Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding 179 (2019), 41 – 65. https://doi.org/10.1016/j.cviu.2018.10.009

Eric Brochu, VladM Cora, and Nando DeFreitas. 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599(2010).

Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018. Large Scale GAN Training for High Fidelity Natural Image Synthesis. arxiv:1809.11096[cs.LG]

Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In International Conference on Learning Representations. https://openreview.net/forum?id=B1xsqj09Fm

Andrew Brock, Theodore Lim, JamesM Ritchie, and Nick Weston. 2016. Neural photo editing with introspective adversarial networks. arXiv preprint arXiv:1609.07093(2016).

Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng(Eds.). 2011. Handbook of Markov Chain Monte Carlo. Chapman and Hall.

Wengling Chen and James Hays. 2018. SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248–255.

Brochu Eric, NandoD Freitas, and Abhijeet Ghosh. 2008. Active preference learning with discrete choice data. In Advances in neural information processing systems. 409–416.

Edoardo Giacomello, PierLuca Lanzi, and Daniele Loiacono. 2019. Searching the Latent Space of a Generative Adversarial Network to Generate DOOM Levels. In 2019 IEEE Conference on Games (CoG). 1–8. https://doi.org/10.1109/CIG.2019.8848011

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27, Z.Ghahramani, M.Welling, C.Cortes, N.D. Lawrence, and K.Q. Weinberger(Eds.). Curran Associates, Inc., 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

Aaron Hertzmann. 2020. Visual Indeterminacy in GAN Art. Leonardo 53, 4 (2020), 424–428. https://doi.org/10.1162/leon_a_01930arXiv:https://doi.org/10.1162/leon_a_01930

MatthewD Hoffman and Andrew Gelman. 2014. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.J. Mach. Learn. Res. 15, 1 (2014), 1593–1623.

Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz. 2018. Multimodal Unsupervised Image-to-image Translation. In Proceedings of the European Conference on Computer Vision (ECCV).

Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. 2020. GANSpace: Discovering Interpretable GAN Controls. In Advances in Neural Information Processing Systems, Vol.33. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2020/file/6fe43269967adbb64ec6149852b5cc3e-Paper.pdf

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and AlexeiA. Efros. 2017. Image-To-Image Translation With Conditional Adversarial Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Mario Klingemann. 2020. Trapping the Accident. In PhotographyDigitalPainting: Expanding Medium Interconnectivity in Contemporary Visual Art Practices(first ed.), Carl Robinson (Ed.). Cambridge Scholars Publishing, 77–98.

Yuki Koyama, Issei Sato, and Masataka Goto. 2020. Sequential Gallery for Interactive Visual Design Optimization. ACM Trans. Graph. 39, 4, Article 88 (July 2020), 12pages. https://doi.org/10.1145/3386569.3392444

Yuki Koyama, Issei Sato, Daisuke Sakamoto, and Takeo Igarashi. 2017. Sequential Line Search for Efficient Visual Design Optimization by Crowds. ACM Trans. Graph. 36, 4, Article 48 (July 2017), 11pages. https://doi.org/10.1145/3072959.3073598

J. Marks, B. Andalman, P.A. Beardsley, W. Freeman, S. Gibson, J. Hodgins, T. Kang, B. Mirtich, H. Pfister, W. Ruml, K. Ryall, J. Seims, and S. Shieber. 1997. Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques(SIGGRAPH ’97). ACM Press/Addison-Wesley Publishing Co., USA, 389–400. https://doi.org/10.1145/258734.258887

Jakob Nielsen and Rolf Molich. 1990. Heuristic Evaluation of User Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA) (CHI ’90). Association for Computing Machinery, New York, NY, USA, 249–256. https://doi.org/10.1145/97243.97281

Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. 2019. Semantic Image Synthesis With Spatially-Adaptive Normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arxiv:1511.06434[cs.LG]

Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. 2016. Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems 29, D.D. Lee, M.Sugiyama, U.V. Luxburg, I.Guyon, and R.Garnett (Eds.). Curran Associates, Inc., 2234–2242. http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf

John Salvatier, ThomasV Wiecki, and Christopher Fonnesbeck. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2(2016), e55.

Helena Sarin. 2019. The Books of GANesis: Divine Comedy in Tangled Representations. Helena Sarin.

Jacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu, Simon Lucas, and Sebastian Risi. 2020. Interactive Evolution and Exploration within Latent Level-Design Space of Generative Adversarial Networks. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (Cancún, Mexico) (GECCO ’20). Association for Computing Machinery, New York, NY, USA, 148–156. https://doi.org/10.1145/3377930.3389821

Ben Shneiderman. 2007. Creativity Support Tools: Accelerating Discovery and Innovation. Commun. ACM 50, 12 (Dec. 2007), 20–32. https://doi.org/10.1145/1323688.1323689

JacobO. Wobbrock, Leah Findlater, Darren Gergle, and JamesJ. Higgins. 2011. The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only Anova Procedures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI ’11). Association for Computing Machinery, New York, NY, USA, 143–146. https://doi.org/10.1145/1978942.1978963

Huikai Wu, Shuai Zheng, Junge Zhang, and Kaiqi Huang. 2019. GP-GAN: Towards Realistic High-Resolution Image Blending. In Proceedings of the 27th ACM International Conference on Multimedia (Nice, France) (MM ’19). Association for Computing Machinery, New York, NY, USA, 2487–2495. https://doi.org/10.1145/3343031.3350944

Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, LiF Fei-Fei, and Michael Bernstein. 2019. HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models. In Advances in Neural Information Processing Systems 32, H.Wallach, H.Larochelle, A.Beygelzimer, F.d'Alché-Buc, E.Fox, and R.Garnett (Eds.). Curran Associates, Inc., 3449–3461. http://papers.nips.cc/paper/8605-hype-a-benchmark-for-human-eye-perceptual-evaluation-of-generative-models.pdf

Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and AlexeiA. Efros. 2016. Generative Visual Manipulation on the Natural Image Manifold. In Computer Vision – ECCV 2016, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling(Eds.). Springer International Publishing, Cham, 597–613.