Paranom: A Parallel Anomaly Dataset Generator
arXiv:1801.03164v13 citations
Originality Synthesis-oriented
AI Analysis
This work addresses the need for better anomaly detection datasets, but it appears incremental as it builds on existing models without introducing major innovations.
The authors tackled the problem of generating parallel anomaly datasets and demonstrated that Paranom improves the classification correctness of the LSTM-AD anomaly detection model.
In this paper, we present Paranom, a parallel anomaly dataset generator. We discuss its design and provide brief experimental results demonstrating its usefulness in improving the classification correctness of LSTM-AD, a state-of-the-art anomaly detection model.