{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:06:20Z","timestamp":1728176780965},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Commun. ACM"],"published-print":{"date-parts":[[2020,10,22]]},"abstract":"\n Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the\n generative modeling<\/jats:italic>\n problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.\n <\/jats:p>","DOI":"10.1145\/3422622","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T18:17:26Z","timestamp":1603390646000},"page":"139-144","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6716,"title":["Generative adversarial networks"],"prefix":"10.1145","volume":"63","author":[{"given":"Ian","family":"Goodfellow","sequence":"first","affiliation":[{"name":"Google Brain"}]},{"given":"Jean","family":"Pouget-Abadie","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]},{"given":"Mehdi","family":"Mirza","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]},{"given":"Bing","family":"Xu","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]},{"given":"David","family":"Warde-Farley","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]},{"given":"Sherjil","family":"Ozair","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]},{"given":"Aaron","family":"Courville","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]},{"given":"Yoshua","family":"Bengio","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Montr\u00e9al"}]}],"member":"320","published-online":{"date-parts":[[2020,10,22]]},"reference":[{"volume-title":"Wasserstein gan. arXiv preprint arXiv:1701.07875","year":"2017","author":"Arjovsky M.","key":"e_1_2_1_1_1","unstructured":"Arjovsky , M. , Chintala , S. , Bottou , L. Wasserstein gan. arXiv preprint arXiv:1701.07875 ( 2017 ). Arjovsky, M., Chintala, S., Bottou, L. Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)."},{"volume-title":"Generalization and equilibrium in generative adversarial nets (gans). arXiv preprint arXiv:1703.00573","year":"2017","author":"Arora S.","key":"e_1_2_1_2_1","unstructured":"Arora , S. , Ge , R. , Liang , Y. , Ma , T. , Zhang , Y. Generalization and equilibrium in generative adversarial nets (gans). arXiv preprint arXiv:1703.00573 ( 2017 ). Arora, S., Ge, R., Liang, Y., Ma, T., Zhang, Y. Generalization and equilibrium in generative adversarial nets (gans). arXiv preprint arXiv:1703.00573 (2017)."},{"volume-title":"Privacy-preserving generative deep neural networks support clinical data sharing. bioRxiv","year":"2017","author":"Beaulieu-Jones B.K.","key":"e_1_2_1_3_1","unstructured":"Beaulieu-Jones , B.K. , Wu , Z.S. , Williams , C. , Greene , C.S. Privacy-preserving generative deep neural networks support clinical data sharing. bioRxiv ( 2017 ), 159756. Beaulieu-Jones, B.K., Wu, Z.S., Williams, C., Greene, C.S. Privacy-preserving generative deep neural networks support clinical data sharing. bioRxiv (2017), 159756."},{"volume-title":"ICML'2014","year":"2014","author":"Bengio Y.","key":"e_1_2_1_4_1","unstructured":"Bengio , Y. , Thibodeau-Laufer , E. , Alain , G. , Yosinski , J. Deep generative stochastic networks trainable by backprop . In ICML'2014 ( 2014 ). Bengio, Y., Thibodeau-Laufer, E., Alain, G., Yosinski, J. Deep generative stochastic networks trainable by backprop. In ICML'2014 (2014)."},{"key":"e_1_2_1_5_1","unstructured":"Brundage M. Avin S. Clark J. Toner H. Eckersley P. Garfinkel B. Dafoe A. Scharre P. Zeitzoff T. Filar B. Anderson H. Roff H. Allen G.C. Steinhardt J. Flynn C. h\u00c9igeartaigh S.\u00d3. Beard S. Belfield H. Farquhar S. Lyle C. Crootof R. Evans O. Page M. Bryson J. Yampolskiy R. Amodei D. The Malicious Use of Artificial Intelligence: Forecasting Prevention and Mitigation. ArXiv e-prints (Feb. 2018). Brundage M. Avin S. Clark J. Toner H. Eckersley P. Garfinkel B. Dafoe A. Scharre P. Zeitzoff T. Filar B. Anderson H. Roff H. Allen G.C. Steinhardt J. Flynn C. h\u00c9igeartaigh S.\u00d3. Beard S. Belfield H. Farquhar S. Lyle C. Crootof R. Evans O. Page M. Bryson J. Yampolskiy R. Amodei D. The Malicious Use of Artificial Intelligence: Forecasting Prevention and Mitigation. ArXiv e-prints (Feb. 2018)."},{"volume-title":"Comparison of maximum likelihood and GAN-based training of real nvps. arXiv preprint arXiv:1705.05263","year":"2017","author":"Danihelka I.","key":"e_1_2_1_6_1","unstructured":"Danihelka , I. , Lakshminarayanan , B. , Uria , B. , Wierstra , D. , Dayan , P. Comparison of maximum likelihood and GAN-based training of real nvps. arXiv preprint arXiv:1705.05263 ( 2017 ). Danihelka, I., Lakshminarayanan, B., Uria, B., Wierstra, D., Dayan, P. Comparison of maximum likelihood and GAN-based training of real nvps. arXiv preprint arXiv:1705.05263 (2017)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"de Oliveira L. Paganini M. Nachman B. Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Computing and Software for Big Science 1 1(2017) 4. de Oliveira L. Paganini M. Nachman B. Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Computing and Software for Big Science 1 1(2017) 4.","DOI":"10.1007\/s41781-017-0004-6"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"volume-title":"International Conference on Learning Representations","year":"2018","author":"Fedus W.","key":"e_1_2_1_9_1","unstructured":"Fedus , W. , Goodfellow , I. , Dai , A.M. Mask GAN : Better text generation via filling in the _____ . In International Conference on Learning Representations ( 2018 ). Fedus, W., Goodfellow, I., Dai, A.M. MaskGAN: Better text generation via filling in the _____. In International Conference on Learning Representations (2018)."},{"volume-title":"International Conference on Learning Representations","year":"2018","author":"Fedus W.","key":"e_1_2_1_10_1","unstructured":"Fedus , W. , Rosca , M. , Lakshminarayanan , B. , Dai , A.M. , Mohamed , S. , Goodfellow , I. Many paths to equilibrium: GANs do not need to decrease a divergence at every step . In International Conference on Learning Representations ( 2018 ). Fedus, W., Rosca, M., Lakshminarayanan, B., Dai, A.M., Mohamed, S., Goodfellow, I. Many paths to equilibrium: GANs do not need to decrease a divergence at every step. In International Conference on Learning Representations (2018)."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/289988"},{"volume-title":"International Conference on Machine Learning","year":"2015","author":"Ganin Y.","key":"e_1_2_1_12_1","unstructured":"Ganin , Y. , Lempitsky , V. Unsupervised domain adaptation by backpropagation . In International Conference on Machine Learning ( 2015 ), 1180--1189. Ganin, Y., Lempitsky, V. Unsupervised domain adaptation by backpropagation. In International Conference on Machine Learning (2015), 1180--1189."},{"key":"e_1_2_1_13_1","first-page":"2680","article-title":"Generative adversarial nets. Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger, eds. 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CoRR, abs\/1710.10196","year":"2017","author":"Karras T.","key":"e_1_2_1_14_1","unstructured":"Karras , T. , Aila , T. , Laine , S. , Lehtinen , J. Progressive growing of GANs for improved quality, stability, and variation. CoRR, abs\/1710.10196 ( 2017 ). Karras, T., Aila, T., Laine, S., Lehtinen, J. Progressive growing of GANs for improved quality, stability, and variation. CoRR, abs\/1710.10196 (2017)."},{"volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","year":"2014","author":"Kingma D.P.","key":"e_1_2_1_15_1","unstructured":"Kingma , D.P. , Welling , M. Auto-encoding variational bayes . In Proceedings of the International Conference on Learning Representations (ICLR) ( 2014 ). Kingma, D.P., Welling, M. Auto-encoding variational bayes. In Proceedings of the International Conference on Learning Representations (ICLR) (2014)."},{"volume-title":"Generative moment matching networks. CoRR, abs\/1502.02761","year":"2015","author":"Li Y.","key":"e_1_2_1_16_1","unstructured":"Li , Y. , Swersky , K. , Zemel , R.S. Generative moment matching networks. CoRR, abs\/1502.02761 ( 2015 ). Li, Y., Swersky, K., Zemel, R.S. Generative moment matching networks. CoRR, abs\/1502.02761 (2015)."},{"key":"e_1_2_1_17_1","first-page":"477","article-title":"Coupled generative adversarial networks. D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, R. Garnett, eds. Advances in Neural Information Processing Systems 29, Curran Associates, Inc","volume":"469","author":"Liu M.-Y.","year":"2016","unstructured":"Liu , M.-Y. , Tuzel , O . Coupled generative adversarial networks. D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, R. Garnett, eds. Advances in Neural Information Processing Systems 29, Curran Associates, Inc ., Boston , 2016 , 469 -- 477 . Liu, M.-Y., Tuzel, O. Coupled generative adversarial networks. D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, R. Garnett, eds. 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Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015)."},{"volume-title":"Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. arXiv preprint arXiv:1701.04722","year":"2017","author":"Mescheder L.","key":"e_1_2_1_20_1","unstructured":"Mescheder , L. , Nowozin , S. , Geiger , A. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. arXiv preprint arXiv:1701.04722 ( 2017 ). Mescheder, L., Nowozin, S., Geiger, A. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. arXiv preprint arXiv:1701.04722 (2017)."},{"volume-title":"Advances in Neural Information Processing Systems","year":"2017","author":"Mescheder L.","key":"e_1_2_1_21_1","unstructured":"Mescheder , L. , Nowozin , S. , Geiger , A. The numerics of gans . In Advances in Neural Information Processing Systems ( 2017 ), 1823--1833. Mescheder, L., Nowozin, S., Geiger, A. The numerics of gans. In Advances in Neural Information Processing Systems (2017), 1823--1833."},{"volume-title":"Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163","year":"2016","author":"Metz L.","key":"e_1_2_1_22_1","unstructured":"Metz , L. , Poole , B. , Pfau , D. , Sohl-Dickstein , J. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 ( 2016 ). Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 (2016)."},{"volume-title":"Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784","year":"2014","author":"Mirza M.","key":"e_1_2_1_23_1","unstructured":"Mirza , M. , Osindero , S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 ( 2014 ). Mirza, M., Osindero, S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)."},{"key":"e_1_2_1_24_1","first-page":"5595","article-title":"Gradient descent GAN optimization is locally stable. I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett, eds. Advances in Neural Information Processing Systems 30, Curran Associates, Inc","volume":"5585","author":"Nagarajan V.","year":"2017","unstructured":"Nagarajan , V. , Kolter , J.Z . Gradient descent GAN optimization is locally stable. I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett, eds. Advances in Neural Information Processing Systems 30, Curran Associates, Inc ., Boston , 2017 , 5585 -- 5595 . Nagarajan, V., Kolter, J.Z. Gradient descent GAN optimization is locally stable. I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett, eds. Advances in Neural Information Processing Systems 30, Curran Associates, Inc., Boston, 2017, 5585--5595.","journal-title":"Boston"},{"volume-title":"Conditional image synthesis with auxiliary classifier gans. arXiv preprint arXiv:1610.09585","year":"2016","author":"Odena A.","key":"e_1_2_1_25_1","unstructured":"Odena , A. , Olah , C. , Shlens , J. Conditional image synthesis with auxiliary classifier gans. arXiv preprint arXiv:1610.09585 ( 2016 ). Odena, A., Olah, C., Shlens, J. Conditional image synthesis with auxiliary classifier gans. arXiv preprint arXiv:1610.09585 (2016)."},{"key":"e_1_2_1_26_1","unstructured":"Oord A. v. d. Li Y. Babuschkin I. Simonyan K. Vinyals O. Kavukcuoglu K. Driessche G. v. d. Lockhart E. Cobo L.C. Stimberg F. et al. Parallel wavenet: Fast high-fidelity speech synthesis. arXiv preprint arXiv:1711.10433 (2017). Oord A. v. d. Li Y. Babuschkin I. Simonyan K. Vinyals O. Kavukcuoglu K. Driessche G. v. d. Lockhart E. Cobo L.C. Stimberg F. et al. Parallel wavenet: Fast high-fidelity speech synthesis. arXiv preprint arXiv:1711.10433 (2017)."},{"volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","year":"2015","author":"Radford A.","key":"e_1_2_1_27_1","unstructured":"Radford , A. , Metz , L. , Chintala , S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Radford, A., Metz, L., Chintala, S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton.2013.6736623"},{"volume-title":"Advances in Neural Information Processing Systems","year":"2016","author":"Salimans T.","key":"e_1_2_1_29_1","unstructured":"Salimans , T. , Goodfellow , I. , Zaremba , W. , Cheung , V. , Radford , A. , Chen , X. Improved techniques for training gans . In Advances in Neural Information Processing Systems ( 2016 ), 2234--2242. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X. Improved techniques for training gans. In Advances in Neural Information Processing Systems (2016), 2234--2242."},{"key":"e_1_2_1_30_1","unstructured":"Shrivastava A. Pfister T. Tuzel O. Susskind J. Wang W. Webb R. Learning from simulated and unsupervised images through adversarial training. Shrivastava A. Pfister T. Tuzel O. Susskind J. Wang W. Webb R. Learning from simulated and unsupervised images through adversarial training."},{"volume-title":"A note on the evaluation of generative models. arXiv:1511.01844 (Nov","year":"2015","author":"Theis L.","key":"e_1_2_1_31_1","unstructured":"Theis , L. , van den Oord , A. , Bethge , M. A note on the evaluation of generative models. arXiv:1511.01844 (Nov 2015 ). Theis, L., van den Oord, A., Bethge, M. A note on the evaluation of generative models. arXiv:1511.01844 (Nov 2015)."},{"volume-title":"Coulomb GANs: Provably optimal Nash equilibria via potential fields. arXiv preprint arXiv:1708.08819","year":"2017","author":"Unterthiner T.","key":"e_1_2_1_32_1","unstructured":"Unterthiner , T. , Nessler , B. , Klambauer , G. , Heusel , M. , Ramsauer , H. , Hochreiter , S. Coulomb GANs: Provably optimal Nash equilibria via potential fields. arXiv preprint arXiv:1708.08819 ( 2017 ). Unterthiner, T., Nessler, B., Klambauer, G., Heusel, M., Ramsauer, H., Hochreiter, S. Coulomb GANs: Provably optimal Nash equilibria via potential fields. arXiv preprint arXiv:1708.08819 (2017)."},{"volume-title":"On the quantitative analysis of decoder-based generative models. arXiv preprint arXiv:1611.04273","year":"2016","author":"Wu Y.","key":"e_1_2_1_33_1","unstructured":"Wu , Y. , Burda , Y. , Salakhutdinov , R. , Grosse , R. On the quantitative analysis of decoder-based generative models. arXiv preprint arXiv:1611.04273 ( 2016 ). Wu, Y., Burda, Y., Salakhutdinov, R., Grosse, R. On the quantitative analysis of decoder-based generative models. arXiv preprint arXiv:1611.04273 (2016)."},{"volume-title":"Semantic image inpainting with perceptual and contextual losses. arXiv preprint arXiv:1607.07539","year":"2016","author":"Yeh R.","key":"e_1_2_1_34_1","unstructured":"Yeh , R. , Chen , C. , Lim , T.Y. , Hasegawa-Johnson , M. , Do , M.N. Semantic image inpainting with perceptual and contextual losses. arXiv preprint arXiv:1607.07539 ( 2016 ). Yeh, R., Chen, C., Lim, T.Y., Hasegawa-Johnson, M., Do, M.N. Semantic image inpainting with perceptual and contextual losses. arXiv preprint arXiv:1607.07539 (2016)."},{"volume-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593","year":"2017","author":"Zhu J.-Y.","key":"e_1_2_1_35_1","unstructured":"Zhu , J.-Y. , Park , T. , Isola , P. , Efros , A.A. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 ( 2017 ). Zhu, J.-Y., Park, T., Isola, P., Efros, A.A. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)."}],"container-title":["Communications of the ACM"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3422622","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T10:46:14Z","timestamp":1698403574000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3422622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,22]]},"references-count":35,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,10,22]]}},"alternative-id":["10.1145\/3422622"],"URL":"http:\/\/dx.doi.org\/10.1145\/3422622","relation":{},"ISSN":["0001-0782","1557-7317"],"issn-type":[{"type":"print","value":"0001-0782"},{"type":"electronic","value":"1557-7317"}],"subject":[],"published":{"date-parts":[[2020,10,22]]},"assertion":[{"value":"2020-10-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}