Many machine learning classification systems lack competency awareness.
Specifically, many systems lack the ability to identify when outliers (e.g.,
samples that are distinct from and not represented in the training data
distribution) are being presented to the system. The ability to detect outliers
is of practical significance since it can help the system behave in an
reasonable way when encountering unexpected data. In prior work, outlier
detection is commonly carried out in a processing pipeline that is distinct
from the classification model. Thus, for a complete system that incorporates
outlier detection and classification, two models must be trained, increasing
the overall complexity of the approach. In this paper we use the concept of the
null space to integrate an outlier detection method directly into a neural
network used for classification. Our method, called Null Space Analysis (NuSA)
of neural networks, works by computing and controlling the magnitude of the
null space projection as data is passed through a network. Using these
projections, we can then calculate a score that can differentiate between
normal and abnormal data. Results are shown that indicate networks trained with
NuSA retain their classification performance while also being able to detect
outliers at rates similar to commonly used outlier detection algorithms.

本文提出一种名为 NuSA 的方法，通过计算和控制神经网络中的零空间投影大小，成功地将异常样本检测方法整合到分类模型中，实现对异常情况的精确识别。实验表明，使用 NuSA 训练的网络既能保持其分类性能，同时也能以类似于常用异常值检测算法的速率检测异常值。