用轻量神经网络替代逻辑回归，提升异常像素检测可靠性。
Neural Network Meta Classifier: Improving the Reliability of Anomaly Segmentation
- 用轻量全连接网络替代传统逻辑回归做元分类。
- 在自动驾驶场景中，异常分割准确率显著优于原方法。
- 适合需要高可靠性的自动驾驶、工业质检等安全敏感领域。
深度神经网络（DNN）广泛用于语义分割，通常在预定义的封闭类别上训练。在开放集环境中，可能遇到语义未知的物体或异常情况。以自动驾驶道路驾驶为例，从安全角度出发，需确保DNN能识别自身已超出学习的语义域。一种常见的异常分割方法是最大熵，结合基于逻辑回归的后处理步骤——元分类，以提高异常像素检测的可靠性。本文提出用更表达能力强的轻量全连接神经网络替代逻辑回归作为元分类器。分析了其优缺点，并证明其性能优于逻辑回归。同时引入‘信息性分布外样本’概念，实验证明该策略能有效提升熵最大化方法的实际训练效果。最后讨论了可解释性损失问题，发现逻辑回归与神经网络的行为高度相关。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) are a contemporary solution for semantic segmentation and are usually trained to operate on a predefined closed set of classes. In open-set environments, it is possible to encounter semantically unknown objects or anomalies. Road driving is an example of such an environment in which, from a safety standpoint, it is important to ensure that a DNN indicates it is operating outside of its learned semantic domain. One possible approach to anomaly segmentation is entropy maximization, which is paired with a logistic regression based post-processing step called meta classification, which is in turn used to improve the reliability of detection of anomalous pixels. We propose to substitute the logistic regression meta classifier with a more expressive lightweight fully connected neural network. We analyze advantages and drawbacks of the proposed neural network meta classifier and demonstrate its better performance over logistic regression. We also introduce the concept of informative out-of-distribution examples which we show to improve training results when using entropy maximization in practice. Finally, we discuss the loss of interpretability and show that the behavior of logistic regression and neural network is strongly correlated.
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