An Approach Towards Architecture-Independent Output for Generative Networks: Texturing Aerial Town Maps for Roleplaying Games

Author:

Siracusa GianfrancoORCID,Seychell DylanORCID,Bugeja MarkORCID

Publisher

Springer International Publishing

Reference35 articles.

1. Siracusa, G., Seychell, D., Bugeja, M.: Blending output from generative adversarial networks to texture high-resolution 2D town maps for roleplaying games. In: 3rd IEEE Conference on Games (CoG) (2021)

2. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)

3. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive Growing of GANs for Improved Quality, Stability, and Variation. International Conference on Learning Representations (2018)

4. Mirza, M, Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411 (2014)

5. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In: Advances in neural information processing systems, pp. 2172–2180 (2016)

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