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.
翻译:神经网络分类器在预测他们经过培训确定的投入类别时,具有很高的准确性。在动态环境中保持这种准确性,因为输入往往不属于固定的、最初已知的类别,这仍然是一个挑战。典型的方法是检测新类的投入,对分类器进行再培训,以强化数据集。然而,不仅分类器,而且检测机制也需要进行调整,以区分新学和未知的投入类别。为了应对这一挑战,我们引入了积极监测神经网络的算法框架。我们框架包装的监视器与神经网络平行运行,并通过一系列可解释的关于渐进适应的标签查询与人类用户互动。此外,我们提议采用适应性数量监测器来提高精确度。对不同类别的各种基准进行实验性评估,证实了我们在动态情景下积极监测框架的好处。