Into the Unknown: Active Monitoring of Neural Networks
This addresses the problem of adapting neural networks to new classes in dynamic settings for AI practitioners, representing an incremental improvement over existing detection and retraining methods.
The paper tackles the challenge of maintaining neural network classifier accuracy in dynamic environments where inputs may belong to novel classes, by introducing an active monitoring framework that interacts with users via interpretable labeling queries for incremental adaptation, resulting in confirmed benefits across diverse benchmarks.
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.