Outside the Box: Abstraction-Based Monitoring of Neural Networks

Abstract

Neural networks have demonstrated unmatched performance in a range of classification tasks. Despite numerous efforts of the research community, novelty detection remains one of the significant limitations of neural networks. The ability to identify previously unseen inputs as novel is crucial for our understanding of the decisions made by neural networks. At runtime, inputs not falling into any of the categories learned during training cannot be classified correctly by the neural network. Existing approaches treat the neural network as a black box and try to detect novel inputs based on the confidence of the output predictions. However, neural networks are not trained to reduce their confidence for novel inputs, which limits the effectiveness of these approaches. We propose a framework to monitor a neural network by observing the hidden layers. We employ a common abstraction from program analysis - boxes - to identify novel behaviors in the monitored layers, i.e., inputs that cause behaviors outside the box. For each neuron, the boxes range over the values seen in training. The framework is efficient and flexible to achieve a desired trade-off between raising false warnings and detecting novel inputs. We illustrate the performance and the robustness to variability in the unknown classes on popular image-classification benchmarks.

accepted at ECAI 2020

References in corpus (6)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Towards A Rigorous Science of Interpretable Machine Learning
• Unsupervised Domain Adaptation by Backpropagation
• On Calibration of Modern Neural Networks
• Selective Classification for Deep Neural Networks
• Online k-means Clustering

Cited by in corpus (5)

• Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends
• Into the Unknown: Active Monitoring of Neural Networks
• Model Assertions for Monitoring and Improving ML Models
• Input Validation for Neural Networks via Runtime Local Robustness Verification
• SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks