With the recent growth in computer vision applications, the question of how
fair and unbiased they are has yet to be explored. There is abundant evidence
that the bias present in training data is reflected in the models, or even
amplified. Many previous methods for image dataset de-biasing, including models
based on augmenting datasets, are computationally expensive to implement. In
this study, we present a fast and effective model to de-bias an image dataset
through reconstruction and minimizing the statistical dependence between
intended variables. Our architecture includes a U-net to reconstruct images,
combined with a pre-trained classifier which penalizes the statistical
dependence between target attribute and the protected attribute. We evaluate
our proposed model on CelebA dataset, compare the results with a
state-of-the-art de-biasing method, and show that the model achieves a
promising fairness-accuracy combination.

本研究通过重构和最小化预期变量之间的统计相依来解决图像训练数据偏见的问题，使用包含 U-net 和预训练分类器的架构，将所提出的模型与最新的去偏见方法进行了对比，并展示了模型的公正性 - 准确性组合。