Built on

Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23
2001
Earlier work this paper cites.
Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. pp. 335–340 (2018)
Original
2003
Earlier work this paper cites.
Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
Earlier work this paper cites.
Pedreshi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 560–568 (2008)
2008
Earlier work this paper cites.
Kamiran, F., Calders, T.: Classifying without discriminating. In: 2009 2nd International Conference on Computer, Control and Communication. pp. 1–6. IEEE (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
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 (2013)
2013
Earlier work this paper cites.

Similar

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)
2014
Cited alongside, same era.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Cited alongside, same era.
Edwards, H., Storkey, A.: Censoring representations with an adversary. arXiv preprint arXiv:1511.05897 (2015)
Original
2015
Cited alongside, same era.
Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.: The variational fair autoencoder. arXiv preprint arXiv:1511.00830 (2015)
Original
2015
Cited alongside, same era.

Then

Moyer, D., Gao, S., Brekelmans, R., Galstyan, A., Ver Steeg, G.: Invariant representations without adversarial training. In: Advances in Neural Information Processing Systems. pp. 9084–9093 (2018)
2018
Later among the works it cites.
Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning. fairmlbook.org (2019), http://www.fairmlbook.org
2019
Later among the works it cites.
Creager, E., Madras, D., Jacobsen, J.H., Weis, M.A., Swersky, K., Pitassi, T., Zemel, R.: Flexibly fair representation learning by disentanglement. arXiv preprint arXiv:1906.02589 (2019)
Original
2019
Later among the works it cites.
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O.: On the fairness of disentangled representations. In: Advances in Neural Information Processing Systems. pp. 14584–14597 (2019)
2019
Later among the works it cites.

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2020

Fairness by Learning Orthogonal Disentangled Representations

Sarhan, Mhd Hasan, Navab, Nassir, Eslami, Abouzar et al.

Understand

Learning discriminative powerful representations is a crucial step for machine learning systems.

Introducing invariance against arbitrary nuisance or sensitive attributes while performing well on specific tasks is an important problem in representation learning.
This is mostly approached by purging the sensitive information from learned representations.
In this paper, we propose a novel disentanglement approach to invariant representation problem.

Built on

Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23
2001
Earlier work this paper cites.
Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. pp. 335–340 (2018)
Original
2003
Earlier work this paper cites.
Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
Earlier work this paper cites.
Pedreshi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 560–568 (2008)
2008
Earlier work this paper cites.
Kamiran, F., Calders, T.: Classifying without discriminating. In: 2009 2nd International Conference on Computer, Control and Communication. pp. 1–6. IEEE (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
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 (2013)
2013
Earlier work this paper cites.

Similar

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)
2014
Cited alongside, same era.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Cited alongside, same era.
Edwards, H., Storkey, A.: Censoring representations with an adversary. arXiv preprint arXiv:1511.05897 (2015)
Original
2015
Cited alongside, same era.
Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.: The variational fair autoencoder. arXiv preprint arXiv:1511.00830 (2015)
Original
2015
Cited alongside, same era.

Then

Moyer, D., Gao, S., Brekelmans, R., Galstyan, A., Ver Steeg, G.: Invariant representations without adversarial training. In: Advances in Neural Information Processing Systems. pp. 9084–9093 (2018)
2018
Later among the works it cites.
Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning. fairmlbook.org (2019), http://www.fairmlbook.org
2019
Later among the works it cites.
Creager, E., Madras, D., Jacobsen, J.H., Weis, M.A., Swersky, K., Pitassi, T., Zemel, R.: Flexibly fair representation learning by disentanglement. arXiv preprint arXiv:1906.02589 (2019)
Original
2019
Later among the works it cites.
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O.: On the fairness of disentangled representations. In: Advances in Neural Information Processing Systems. pp. 14584–14597 (2019)
2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision. pp. 630–645. Springer (2016)

2016

Cited alongside, same era.

Dua, D., Graff, C.: Uci machine learning repository (2017)

2017

Cited alongside, same era.

Xie, Q., Dai, Z., Du, Y., Hovy, E., Neubig, G.: Controllable invariance through adversarial feature learning. In: Advances in Neural Information Processing Systems. pp. 585–596 (2017)

2017

Cited alongside, same era.

Madras, D., Creager, E., Pitassi, T., Zemel, R.: Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309 (2018)

Original

2018

Cited alongside, same era.

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)

Original

2019

Later among the works it cites.

Quadrianto, N., Sharmanska, V., Thomas, O.: Discovering fair representations in the data domain. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8227–8236 (2019)

2019

Later among the works it cites.

Roy, P.C., Boddeti, V.N.: Mitigating information leakage in image representations: A maximum entropy approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2586–2594 (2019)

2019

Later among the works it cites.