论文标题
进入未知：神经网络的主动监测
Into the Unknown: Active Monitoring of Neural Networks
论文作者
论文摘要
在预测训练以识别的输入的类别时，神经网络分类器具有很高的精度。在动态环境中保持这种准确性，在动态环境中，输入经常超出固定的最初已知类别，这仍然是一个挑战。典型的方法是检测新型类别的输入，并在增强数据集中重新训练分类器。但是，不仅分类器，而且检测机制还需要适应，以区分新学习的和未知的输入类。为了应对这一挑战，我们引入了一个算法框架，用于主动监测神经网络。包裹在我们框架中的监视器与神经网络并行运行，并通过一系列可解释的标签查询与人类用户进行交互，以增量适应。此外，我们提出了一个自适应定量监视器以提高精度。对具有不同类别的各种基准测试的实验评估证实了我们在动态场景中主动监测框架的好处。
Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. The typical approach is to detect inputs from novel classes and retrain the classifier on an augmented dataset. However, not only the classifier but also the detection mechanism needs to adapt in order to distinguish between newly learned and yet unknown input classes. To address this challenge, we introduce an algorithmic framework for active monitoring of a neural network. A monitor wrapped in our framework operates in parallel with the neural network and interacts with a human user via a series of interpretable labeling queries for incremental adaptation. In addition, we propose an adaptive quantitative monitor to improve precision. An experimental evaluation on a diverse set of benchmarks with varying numbers of classes confirms the benefits of our active monitoring framework in dynamic scenarios.
