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.

翻译：计算机视觉系统中的偏差可能会延续甚至放大对特定群体的歧视。考虑到偏差通常源于有偏的视觉数据集，近期许多研究聚焦于利用此类数据训练公平模型。然而，大多数方法严重依赖数据集中受保护属性标签的可用性，这限制了其适用性，而无标签方法（即无需此类标签运行的方法）性能显著较低。为克服这些局限，本文提出了FLAC——一种无需属性标签即可最小化模型提取特征与受保护属性间互信息的方法。为此，FLAC设计了一种采样策略，突出数据集中代表性不足的样本，并将公平表示学习问题转化为概率匹配问题，该问题利用偏差捕获分类器提取的表示。理论证明表明，FLAC确实能生成与受保护属性无关的公平表示。在偏置MNIST、CelebA和UTKFace数据集上，FLAC分别以29.1%、18.1%和21.9%的幅度超越当前最先进方法。此外，在包含ImageNet最具挑战性样本的ImageNet-A上，FLAC的准确率提升2.2%。最后，在多数实验中，FLAC甚至优于依赖偏置标签的现有最优方法。