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Fairness by Learning Orthogonal Disentangled Representations
[article]
2020
arXiv
pre-print
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. We disentangle the meaningful and sensitive
arXiv:2003.05707v3
fatcat:o6ccgjy2enbvjapgbb5k2cu7ay