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Bias mitigation in image classification has been widely researched, andexisting methods have yielded notable results. However, most of these methodsimplicitly assume that a given image contains only one type of known or unknownbias, failing to consider the complexities of real-world biases. We introduce amore challenging scenario, agnostic biases mitigation, aiming at bias removalregardless of whether the type of bias or the number of types is unknown in thedatasets. To address this difficult task, we present the Partition-and-Debias(PnD) method that uses a mixture of biases-specific experts to implicitlydivide the bias space into multiple subspaces and a gating module to find aconsensus among experts to achieve debiased classification. Experiments on bothpublic and constructed benchmarks demonstrated the efficacy of the PnD. Code isavailable at: https://github.com/Jiaxuan-Li/PnD.

Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts