使用VAE的人脸检测系统去偏
De-biasing facial detection system using VAE
AI总结：
本文提出利用VAE生成模型学习人脸数据潜在特征以去除数据集偏见，并通过在两个数据集上训练和比较验证去偏效果。
摘要：
基于AI/ML的系统中的偏见是一个普遍存在的问题，AI/ML系统中的偏见可能对社会产生负面影响。系统存在偏见的原因有很多。偏见可能是由于我们用于解决问题的算法造成的，也可能是由于我们使用的数据集中某些特征被过度表示。在人脸检测系统中，由于数据集导致的偏见主要可见。有时模型只学习数据中过度表示的特征，而忽略数据中的稀有特征，从而导致偏向那些过度表示的特征。在现实生活中，这些有偏见的系统对社会是危险的。所提出的方法使用生成模型，这些模型最适合从数据集中学习潜在特征（潜在变量），通过使用这些学习到的特征，模型试图减少系统中因偏见而存在的威胁。借助一种算法，可以去除数据集中存在的偏见。然后我们在两个数据集上训练模型并比较结果。
英文摘要：
Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society. There are many reasons behind a system being biased. The bias can be due to the algorithm we are using for our problem or may be due to the dataset we are using, having some features over-represented in it. In the face detection system bias due to the dataset is majorly seen. Sometimes models learn only features that are over-represented in data and ignore rare features from data which results in being biased toward those over-represented features. In real life, these biased systems are dangerous to society. The proposed approach uses generative models which are best suited for learning underlying features(latent variables) from the dataset and by using these learned features models try to reduce the threats which are there due to bias in the system. With the help of an algorithm, the bias present in the dataset can be removed. And then we train models on two datasets and compare the results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
