Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of
  Biases-Specific Experts
Jiaxuan Li Duc Minh Vo Hideki Nakayama
Bias mitigation in image classification has been widely researched, and
existing methods have yielded notable results. However, most of these methods
implicitly assume that a given image contains only one type of known or unknown
bias, failing to consider the complexities of real-world biases. We introduce a
more challenging scenario, agnostic biases mitigation, aiming at bias removal
regardless of whether the type of bias or the number of types is unknown in the
datasets. To address this difficult task, we present the Partition-and-Debias
(PnD) method that uses a mixture of biases-specific experts to implicitly
divide the bias space into multiple subspaces and a gating module to find a
consensus among experts to achieve debiased classification. Experiments on both
public and constructed benchmarks demonstrated the efficacy of the PnD. Code is
available at: https://github.com/Jiaxuan-Li/PnD.