Bias in computer vision systems can perpetuate or even amplify discrimination against certain populations. Considering that bias is often introduced by biased visual datasets, many recent research efforts focus on training fair models using such data. However, most of them heavily rely on the availability of protected attribute labels in the dataset, which limits their applicability, while label-unaware approaches, i.e., approaches operating without such labels, exhibit considerably lower performance. To overcome these limitations, this work introduces FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels. To do that, FLAC proposes a sampling strategy that highlights underrepresented samples in the dataset, and casts the problem of learning fair representations as a probability matching problem that leverages representations extracted by a bias-capturing classifier. It is theoretically shown that FLAC can indeed lead to fair representations, that are independent of the protected attributes. FLAC surpasses the current state-of-the-art on Biased MNIST, CelebA, and UTKFace, by 29.1%, 18.1%, and 21.9%, respectively. Additionally, FLAC exhibits 2.2% increased accuracy on ImageNet-A consisting of the most challenging samples of ImageNet. Finally, in most experiments, FLAC even outperforms the bias label-aware state-of-the-art methods.

通过采用深度学习方法，提出了一种无需保护性特征标签的新型算法FLAC，可以减少因数据集偏见引起的对某些人群的歧视。实验证明，FLAC在MNIST、CelebA和UTKFace数据集上实现的结果都优于现有的最先进水平，而在ImageNet上也能取得很好的性能表现。

FLAC: 抑制属性-类别关联性以实现公平感知的表示学习