  Deep image classifiers have been found to learn biases from datasets. To
mitigate the biases, most previous methods require labels of protected
attributes (e.g., age, skin tone) as full-supervision, which has two
limitations: 1) it is infeasible when the labels are unavailable; 2) they are
incapable of mitigating unknown biases -- biases that humans do not
preconceive. To resolve those problems, we propose Debiasing Alternate Networks
(DebiAN), which comprises two networks -- a Discoverer and a Classifier. By
training in an alternate manner, the discoverer tries to find multiple unknown
biases of the classifier without any annotations of biases, and the classifier
aims at unlearning the biases identified by the discoverer. While previous
works evaluate debiasing results in terms of a single bias, we create
Multi-Color MNIST dataset to better benchmark mitigation of multiple biases in
a multi-bias setting, which not only reveals the problems in previous methods
but also demonstrates the advantage of DebiAN in identifying and mitigating
multiple biases simultaneously. We further conduct extensive experiments on
real-world datasets, showing that the discoverer in DebiAN can identify unknown
biases that may be hard to be found by humans. Regarding debiasing, DebiAN
achieves strong bias mitigation performance.
