Learning Stable Classifiers by Transferring Unstable Features. (arXiv:2106.07847v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2106.07847

While unbiased machine learning models are essential for many applications,
bias is a human-defined concept that can vary across tasks. Given only
input-label pairs, algorithms may lack sufficient information to distinguish
stable (causal) features from unstable (spurious) features. However, related
tasks often share similar biases -- an observation we may leverage to develop
stable classifiers in the transfer setting. In this work, we explicitly inform
the target classifier about unstable features in the source tasks.
Specifically, we derive a representation …

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