{"type":"ScholarlyArticle","title":"Learning Generative Adversarial Networks from Multiple Data Sources","authors":[{"name":"Trung Le","slug":"trung-le","given":"Trung","family":"Le","affiliation":"Faculty of Infomation Technology , Monash University , Australia"},{"name":"Quan Hoang","slug":"quan-hoang","given":"Quan","family":"Hoang","affiliation":"Faculty of Infomation Technology , Monash University , Australia"},{"name":"Hung Vu","slug":"hung-vu","given":"Hung","family":"Vu","affiliation":"AI Research Lab , Trusting Social , Australia"},{"name":"Tu Dinh Nguyen","slug":"tu-dinh-nguyen","given":"Tu Dinh","family":"Nguyen","affiliation":"Faculty of Infomation Technology , Monash University , Australia"},{"name":"Hung Bui","slug":"hung-bui","given":"Hung","family":"Bui","affiliation":"Google DeepMind"},{"name":"Dinh Phung","slug":"dinh-phung","given":"Dinh","family":"Phung","affiliation":"Faculty of Infomation Technology , Monash University , Australia"}],"abstract":"Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.","doi":"10.24963/ijcai.2019/391","url":"https://ijcai.org/proceedings/2019/391","pdf":"https://ijcai.org/proceedings/2019/0391.pdf","markdown":"https://ijcai.org/proceedings/2019/391.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/391","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":391,"pages":{"first":2823,"last":2829},"keywords":["Machine Learning: Adversarial Machine Learning","Machine Learning: Learning Generative Models"],"videos":[],"references":[{"position":1,"raw":"[Arjovsky et al., 2017] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein gan. arXiv preprint arXiv:1701.07875, 2017.","title":"Wasserstein gan","authors":"Martin Arjovsky, Soumith Chintala, Léon Bottou","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2016] Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. In- fogan: Interpretable representation learning by informa- tion maximizing generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2172- 2180, 2016.","title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","authors":"Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Coates et al., 2011] Adam Coates, Andrew Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the four- teenth international conference on artificial intelligence and statistics, pages 215-223, 2011.","title":"An analysis of single-layer networks in unsupervised feature learning","authors":"Adam Coates, Andrew Ng, Honglak Lee","year":"2011","venue":"Proceedings of the fourteenth international conference on artificial intelligence and statistics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dam et al., 2019] Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, and Dinh Phung. Three- player wasserstein gan via amortised duality. In Proc. of the 28th Int. Joint Conf. on Artificial Intelligence (IJCAI), 2019.","title":"Threeplayer wasserstein gan via amortised duality","authors":"Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung","year":"2019","venue":"Proc. of the 28th Int. Joint Conf. on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Goodfellow, 2016] Ian Goodfellow. Nips 2016 tuto- rial: Generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016.","title":"Nips 2016 tutorial: Generative adversarial networks","authors":"Ian Goodfellow","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gulrajani et al., 2017] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. Improved training of wasserstein gans. In Ad- vances in Neural Information Processing Systems, pages 5769-5779, 2017.","title":"Improved training of wasserstein gans","authors":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Aaron C Vincent Dumoulin, Courville","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Heusel et al., 2017] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochre- iter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neu- ral Information Processing Systems, pages 6629-6640, 2017.","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","authors":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hoang et al., 2018] Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung. MGAN: Training generative adver- sarial nets with multiple generators. In International Con- ference on Learning Representations, 2018.","title":"MGAN: Training generative adversarial nets with multiple generators","authors":"Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Isola et al., 2017] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with con- ditional adversarial networks. arXiv preprint, 2017.","title":"Image-to-image translation with conditional adversarial networks","authors":"Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A Efros","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Krizhevsky and Hinton, 2009] Alex Krizhevsky and Geof- frey Hinton. Learning multiple layers of features from tiny images. 2009.","title":"Learning multiple layers of features from tiny images","authors":"Alex Krizhevsky, Geoffrey Hinton","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural in- formation processing systems, pages 1097-1105, 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton","year":"2012","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Le et al., 2018] Trung Le, Hung Vu, Tu Dinh Nguyen, and Dinh Phung. Geometric enclosing networks. In Proc. of the 27th Int. Joint Conf. on Artificial Intelligence (IJCAI), 2018.","title":"Geometric enclosing networks","authors":"Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Phung","year":"2018","venue":"Proc. of the 27th Int. Joint Conf. on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2018/326"},{"position":14,"raw":"[Liu et al., 2015] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision, pages 3730-3738, 2015.","title":"Deep learning face attributes in the wild","authors":"Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2015","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mathieu et al., 2015] Michael Mathieu, Camille Couprie, and Yann LeCun. Deep multi-scale video prediction be- yond mean square error. arXiv preprint arXiv:1511.05440, 2015.","title":"Deep multi-scale video prediction beyond mean square error","authors":"Michael Mathieu, Camille Couprie, Yann Lecun","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Metz et al., 2016] Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163, 2016.","title":"Unrolled generative adversarial networks","authors":"Metz","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mirza and Osindero, 2014] Mehdi Mirza and Simon Osin- dero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.","title":"Conditional generative adversarial nets","authors":"Mehdi Mirza, Simon Osindero","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Miyato et al., 2018] Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. Spectral normal- ization for generative adversarial networks. International Conference on Learning Representations, 2018.","title":"Spectral normalization for generative adversarial networks","authors":"Miyato","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Nguyen et al., 2017] Tu Dinh Nguyen, Trung Le, Hung Vu, and Dinh Phung. Dual discriminator generative adversar- ial nets. In Advances in Neural Information Processing Systems 29 (NIPS), 2017.","title":"Dual discriminator generative adversarial nets","authors":"Trung Tu Dinh Nguyen, Hung Le, Dinh Vu, Phung","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Nowozin et al., 2016] Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. f-gan: Training generative neu- ral samplers using variational divergence minimization. In Advances in Neural Information Processing Systems, pages 271-279, 2016.","title":"f-gan: Training generative neural samplers using variational divergence minimization","authors":"Sebastian Nowozin, Botond Cseke, Ryota Tomioka","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Radford et al., 2015] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.","title":"Unsupervised representation learning with deep convolutional generative adversarial networks","authors":"Radford","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Roth et al., 2017] Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. Stabilizing training of generative adversarial networks through regularization. In Advances in Neural Information Processing Systems 30, pages 2018-2028. 2017.","title":"Stabilizing training of generative adversarial networks through regularization","authors":"Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Russakovsky et al., 2015] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recogni- tion challenge. International Journal of Computer Vision, 115(3):211-252, 2015.","title":"Imagenet large scale visual recognition challenge","authors":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein","year":"2015","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Salimans et al., 2016] Tim Salimans, Ian Goodfellow, Woj- ciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 2234- 2242, 2016.","title":"Improved techniques for training gans","authors":"Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Warde-Farley and Bengio, 2016] David Warde-Farley and Yoshua Bengio. Improving generative adversarial net- works with denoising feature matching. 2016.","title":"Improving generative adversarial networks with denoising feature matching","authors":"Warde-Farley, Bengio ; David Warde-Farley, Yoshua Bengio","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}