Visual recognition models are prone to learning spurious correlations induced by an imbalanced training set where certain groups (\eg Females) are under-represented in certain classes (\eg Programmers). Generative models offer a promising direction in mitigating this bias by generating synthetic data for the minority samples and thus balancing the training set. However, prior work that uses these approaches overlooks that visual recognition models could often learn to differentiate between real and synthetic images and thus fail to unlearn the bias in the original dataset. In our work, we propose a novel two-stage pipeline to mitigate this issue where 1) we pre-train a model on a balanced synthetic dataset and then 2) fine-tune on the real data. Using this pipeline, we avoid training on both real and synthetic data, thus avoiding the bias between real and synthetic data. Moreover, we learn robust features against the bias in the first step that mitigate the bias in the second step. Moreover, our pipeline naturally integrates with bias mitigation methods; they can be simply applied to the fine-tuning step. As our experiments prove, our pipeline can further improve the performance of bias mitigation methods obtaining state-of-the-art performance on three large-scale datasets.

使用合成数据进行预训练并在真实数据上进行微调的新型两阶段方法在减轻视觉识别模型的偏见方面取得了显著的改善，能够与偏见减轻方法整合并获得在三个大规模数据集上的最新性能。

从虚拟到真实（FFR）：利用合成数据进行减弱虚假相关性的两阶段训练管道