Neural Network Meta Classifier: Improving the Reliability of Anomaly Segmentation
This work addresses safety-critical anomaly detection in autonomous driving, but it is incremental as it builds on existing entropy maximization and meta classification methods.
The paper tackled the problem of improving anomaly segmentation reliability in open-set environments like road driving by replacing a logistic regression meta classifier with a lightweight neural network, demonstrating better performance and introducing informative out-of-distribution examples to enhance training.
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.