论文标题
卷积神经网络识别以非人类样的方式工作
Convolutional neural net face recognition works in non-human-like ways
论文作者
论文摘要
卷积神经网络（CNN）在许多模式识别问题中提供了最先进的表现，但可以通过精心制作的噪音模式来欺骗。我们报告说，CNN面部识别系统也会造成令人惊讶的“错误”。我们测试了六个商业面部识别CNN，发现他们在标准面对匹配任务上的表现优于典型的人类参与者。但是，他们还宣布匹配人类不会，其中一对中的一张图像已转化为看起来不同的性别或种族。这不是由于性能差。最好的CNN几乎在人脸匹配的任务上表现出色，但同时也宣布了最明显的种族或性别的面孔的最多匹配。尽管性别和种族的显着性有所不同，但人类和计算机系统并未以完全不同的方式工作。他们倾向于难以发现相同的图像对，这表明对基本相似空间的一致性一致。
Convolutional neural networks (CNNs) give state of the art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising "errors". We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to look a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space.
