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Deep Learning in the Wild
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.
Automated Machine Learning in Practice: State of the Art and Recent Results
A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions.
Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data
In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space.
Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation
To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN.
Learning Informative Health Indicators Through Unsupervised Contrastive Learning
Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods.
