Self-supervised Learning for Anomaly Detection in Computational Workflows

no code implementations • 2 Oct 2023 • Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash

To address this problem, we introduce an autoencoder-driven self-supervised learning~(SSL) approach that learns a summary statistic from unlabeled workflow data and estimates the normal behavior of the computational workflow in the latent space.

Anomaly Detection Contrastive Learning +1