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Learning Unbiased Representations via Mutual Information Backpropagation
[article]
2020
arXiv
pre-print
We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely compromise its generalization properties. We tackle this problem through the lens of information theory, leveraging recent findings for a differentiable estimation of mutual information. We propose a novel end-to-end optimization strategy, which
arXiv:2003.06430v1
fatcat:o2v3jf2jfbebdlkww2ndo5lotm