# Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts Source: [Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:40:08.455Z Authors: Jiaxuan Li, Duc Minh Vo, Hideki Nakayama Published: 2023-08-19T13:11:40.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()