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

Understand

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.