NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection
NV-Tesseract-AD is a diffusion model framework that uses a dual curriculum to train for multivariate time-series anomaly detection. By structuring training difficulty across masking severity and noise level simultaneously, the method stabilizes learning and creates reconstruction methods that are robust to different types of corruption. This leads to significant performance gains in detecting anomalies in complex time series.