论文标题
深层生成观点，以减轻性别分类跨性别率群体的偏见
Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups
论文作者
论文摘要
已发表的研究表明，基于性别的性别率组的基于自动面部的性别分类算法的偏见。具体而言，女性和黑皮肤的人获得了不平等的准确率。为了减轻性别分类器的偏见，愿景社区已经制定了多种策略。但是，这些缓解策略的功效对于有限数量的种族证明了主要是高加索和非裔美国人的功效。此外，这些策略通常在偏见和分类准确性之间提供权衡。为了进一步推进最先进的事物，我们利用生成观点，结构化学习和证据学习的力量来减轻性别分类偏见。我们通过广泛的实验验证来证明我们的偏见缓解策略在提高分类准确性和降低性别 - 种族群体之间的偏见方面的优势，从而在内部和交叉数据集评估中提高了最新性能。
Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people. To mitigate the bias of gender classifiers, the vision community has developed several strategies. However, the efficacy of these mitigation strategies is demonstrated for a limited number of races mostly, Caucasian and African-American. Further, these strategies often offer a trade-off between bias and classification accuracy. To further advance the state-of-the-art, we leverage the power of generative views, structured learning, and evidential learning towards mitigating gender classification bias. We demonstrate the superiority of our bias mitigation strategy in improving classification accuracy and reducing bias across gender-racial groups through extensive experimental validation, resulting in state-of-the-art performance in intra- and cross dataset evaluations.
