{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:30Z","timestamp":1750220370501,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":7,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"FCT - Foundation for Science and Technology","award":["DSAIPA\/DS\/0022\/2018 (GADgET)"],"award-info":[{"award-number":["DSAIPA\/DS\/0022\/2018 (GADgET)"]}]},{"name":"European Social Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,7]]},"DOI":"10.1145\/3449726.3459448","type":"proceedings-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T14:49:57Z","timestamp":1625755797000},"page":"145-146","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Improved evolution of generative adversarial networks"],"prefix":"10.1145","author":[{"given":"Victor","family":"Costa","sequence":"first","affiliation":[{"name":"University of Coimbra, Coimbra, Portugal"}]},{"given":"Nuno","family":"Louren\u00e7o","sequence":"additional","affiliation":[{"name":"University of Coimbra, Coimbra, Portugal"}]},{"given":"Jo\u00e3o","family":"Correia","sequence":"additional","affiliation":[{"name":"University of Coimbra, Coimbra, Portugal"}]},{"given":"Penousal","family":"Machado","sequence":"additional","affiliation":[{"name":"University of Coimbra, Coimbra, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis. In 7th International Conference on Learning Representations, ICLR 2019","author":"Brock Andrew","year":"2019","unstructured":"Andrew Brock , Jeff Donahue , and Karen Simonyan . 2019 . Large Scale GAN Training for High Fidelity Natural Image Synthesis. In 7th International Conference on Learning Representations, ICLR 2019 , New Orleans, LA, USA , May 6-9, 2019. Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019."},{"key":"e_1_3_2_1_2_1","volume-title":"Co-evolution of Generative Adversarial Networks. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, 473--487","author":"Costa Victor","year":"2019","unstructured":"Victor Costa , Nuno Louren\u00e7o , and Penousal Machado . 2019 . Co-evolution of Generative Adversarial Networks. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, 473--487 . Victor Costa, Nuno Louren\u00e7o, and Penousal Machado. 2019. Co-evolution of Generative Adversarial Networks. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, 473--487."},{"key":"e_1_3_2_1_3_1","unstructured":"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. 2672--2680.  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. 2672--2680."},{"key":"e_1_3_2_1_4_1","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems. 6629--6640.  Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems. 6629--6640."},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Learning Representations.","author":"Jolicoeur-Martineau Alexia","year":"2019","unstructured":"Alexia Jolicoeur-Martineau . 2019 . The relativistic discriminator: a key element missing from standard GAN . In International Conference on Learning Representations. Alexia Jolicoeur-Martineau. 2019. The relativistic discriminator: a key element missing from standard GAN. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_6_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato , Toshiki Kataoka , Masanori Koyama , and Yuichi Yoshida . 2018. Spectral Normalization for Generative Adversarial Networks . In 6th International Conference on Learning Representations, ICLR 2018 , Vancouver, BC , Canada, April 30 - May 3, 2018, Conference Track Proceedings . Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral Normalization for Generative Adversarial Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings."},{"key":"e_1_3_2_1_7_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford Alec","year":"2015","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)."}],"event":{"name":"GECCO '21: Genetic and Evolutionary Computation Conference","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"Lille France","acronym":"GECCO '21"},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3449726.3459448","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3449726.3459448","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:37Z","timestamp":1750191457000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3449726.3459448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":7,"alternative-id":["10.1145\/3449726.3459448","10.1145\/3449726"],"URL":"https:\/\/doi.org\/10.1145\/3449726.3459448","relation":{},"subject":[],"published":{"date-parts":[[2021,7,7]]},"assertion":[{"value":"2021-07-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}