论文标题
了解神经网络中罕见的虚假相关性
Understanding Rare Spurious Correlations in Neural Networks
论文作者
论文摘要
已知神经网络使用虚假的相关性，例如背景信息进行分类。虽然先前的工作已经研究了培训数据中广泛存在的虚假相关性，但在这项工作中，我们研究了敏感的神经网络对罕见的伪造相关性是多么难以检测和纠正，并可能导致隐私泄漏。我们介绍了与固定类相关的虚假模式，以示一些培训示例，发现网络仅需几个这样的示例才能学习相关性。此外，这些罕见的虚假相关性也影响了准确性和隐私。我们从经验和理论上分析了与罕见的虚假相关性有关的不同因素，并提出了缓解方法。具体而言，我们观察到$ \ ell_2 $正则化并在输入中添加高斯噪声可以减少不良效果。代码可在https://github.com/yangarbiter/rare-spurious-corteration中获得。
Neural networks are known to use spurious correlations such as background information for classification. While prior work has looked at spurious correlations that are widespread in the training data, in this work, we investigate how sensitive neural networks are to rare spurious correlations, which may be harder to detect and correct, and may lead to privacy leaks. We introduce spurious patterns correlated with a fixed class to a few training examples and find that it takes only a handful of such examples for the network to learn the correlation. Furthermore, these rare spurious correlations also impact accuracy and privacy. We empirically and theoretically analyze different factors involved in rare spurious correlations and propose mitigation methods accordingly. Specifically, we observe that $\ell_2$ regularization and adding Gaussian noise to inputs can reduce the undesirable effects. Code available at https://github.com/yangarbiter/rare-spurious-correlation.
