suggests that the considered technique of self- supervised learning is well suited for high-performance applications involving … techniques. Our model combines a suitable relaxation of the objective function to a continuous solution space with a self-supervised … learning method for neural networks, which does not require any labeled training data. Thereby, we avoid the pre-computation of …

, data preprocessing, transfer learning, semi-supervised learning, machine ​learning ​operations (MLOps), and the effect of …

Self-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning … for multivariate statistical in-process control (MSPC) based on self-supervised learning. Our motivation for SSMSPC is to … leverage the potential of unsupervised representation learning by incorporating self-supervised learning into the general …