Deep generative models require large amounts of training data. This often poses a problem as the collection of datasets can be expensive and difficult, in particular datasets that are representative of the appropriate underlying distribution (e.g. demographic). This introduces biases in datasets which are further propagated in the models. We present an approach to mitigate biases in an existing generative adversarial network by rebalancing the model distribution. We do so by generating balanced data from an existing unbalanced deep generative model using latent space exploration and using this data to train a balanced generative model. Further, we propose a bias mitigation loss function that shows improvements in the fairness metric even when trained with unbalanced datasets. We show results for the Stylegan2 models while training on the FFHQ dataset for racial fairness and see that the proposed approach improves on the fairness metric by almost 5 times, whilst maintaining image quality. We further validate our approach by applying it to an imbalanced Cifar-10 dataset. Lastly, we argue that the traditionally used image quality metrics such as Frechet inception distance (FID) are unsuitable for bias mitigation problems.

翻译：深度生成模型需要大量训练数据。这通常会导致问题，因为数据集的收集可能成本高昂且困难，尤其是那些能代表适当底层分布（例如人口统计）的数据集。这会在数据集中引入偏差，并进一步传播到模型中。我们提出了一种方法，通过重新平衡模型分布来减轻现有生成对抗网络中的偏差。为此，我们利用潜在空间探索从现有的不平衡深度生成模型中生成平衡数据，并使用这些数据训练一个平衡生成模型。此外，我们提出了一种偏差缓解损失函数，即使在使用不平衡数据集训练时，也能在公平性指标上展现出改进。我们在FFHQ数据集上训练Stylegan2模型，展示了种族公平性的结果，并观察到所提出的方法在几乎保持图像质量的同时，将公平性指标提升了近5倍。我们进一步通过将方法应用于不平衡的Cifar-10数据集来验证其有效性。最后，我们认为传统使用的图像质量指标（如弗雷歇初始距离FID）不适用于偏差缓解问题。