RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86

1998

Earlier work this paper cites.

Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)

2011

Earlier work this paper cites.

Berk, R.: Criminal justice forecasts of risk: A machine learning approach. Springer Science & Business Media (2012)

2012

Earlier work this paper cites.

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214–226 (2012)

2012

Earlier work this paper cites.

Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowledge and information systems 33

2012

Earlier work this paper cites.

Zemel, R., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: International conference on machine learning. pp. 325–333. PMLR (2013)

2013

Earlier work this paper cites.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27

2014

Earlier work this paper cites.

Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

Original

2014

Earlier work this paper cites.

Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)

Original

2015

Earlier work this paper cites.

Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. pp. 308–318 (2016)

2016

Earlier work this paper cites.

Barocas, S., Selbst, A.D.: Big data’s disparate impact. Calif. L. Rev. 104

2016

Cited alongside, same era.

Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Advances in neural information processing systems 29

2016

Cited alongside, same era.

Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)

Original

2017

Cited alongside, same era.

Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining. pp. 797–806 (2017)

2017

Cited alongside, same era.

Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)

Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)

2019

Later among the works it cites.

Tanaka, F.H.K.d.S., Aranha, C.: Data augmentation using gans. arXiv preprint arXiv:1904.09135 (2019)

Original

2019

Later among the works it cites.

Xu, D., Yuan, S., Zhang, L., Wu, X.: Fairgan+: Achieving fair data generation and classification through generative adversarial nets. In: 2019 IEEE International Conference on Big Data (Big Data). pp. 1401–1406. IEEE (2019)

2019

Later among the works it cites.

Caton, S., Haas, C.: Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 (2020)

Original

2020

Later among the works it cites.