We describe Fibres of Failure (FiFa), a method to classify failure modes of
predictive processes using the Mapper algorithm from Topological Data Analysis.
Our method uses Mapper to build a graph model of input data stratified by
prediction error.
Groupings found in high-error regions of the Mapper model then provide
distinct failure modes of the predictive process.
We demonstrate FiFa on misclassifications of MNIST images with added noise,
and demonstrate two ways to use the failure mode classification: either to
produce a correction layer that adjusts predictions by similarity to the
failure modes; or to inspect members of the failure modes to illustrate and
investigate what characterizes each failure mode.

使用拓扑数据分析中的 Mapper 算法，本文提出了一种名为 FiFa 的方法，用于分类预测过程的失败模式。在 MNIST 图像添加噪声的情况下，通过对高误差区域的群集分析，可得到预测过程的区别失败模式。本文还演示了两种使用失败模式分类的方法：一是产生一层校正层以通过失败模式的相似性来调整预测，另一种方法是检查失败模式的成员，以说明并调查每种失败模式的特点。