论文标题
模型调试中的失踪性偏见
Missingness Bias in Model Debugging
论文作者
论文摘要
缺失或输入中没有功能是许多模型调试工具的概念。但是，在计算机视觉中，像素不能简单地从图像中删除。因此，一种倾向于诉诸启发式方法，例如将像素涂成黑色，这反过来又可能引入调试过程中的偏见。我们研究了这样的偏见，特别是展示了基于变压器的架构如何实现遗失性的更自然的实施，从而辅助这些问题并提高实践中模型调试的可靠性。我们的代码可从https://github.com/madrylab/missingness获得
Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process. We study such biases and, in particular, show how transformer-based architectures can enable a more natural implementation of missingness, which side-steps these issues and improves the reliability of model debugging in practice. Our code is available at https://github.com/madrylab/missingness
