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 …

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 …

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 …

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 …

the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images … extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised … learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global …