Modeling the distribution of normal data in pre-trained deep features for anomaly detection
Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …
identifying images and/or image substructures that deviate significantly from the norm …
Deep variational graph convolutional recurrent network for multivariate time series anomaly detection
W Chen, L Tian, B Chen, L Dai… - … on machine learning, 2022 - proceedings.mlr.press
Anomaly detection within multivariate time series (MTS) is an essential task in both data
mining and service quality management. Many recent works on anomaly detection focus on …
mining and service quality management. Many recent works on anomaly detection focus on …
Printed circuit board defect detection using deep learning via a skip-connected convolutional autoencoder
As technology evolves, more components are integrated into printed circuit boards (PCBs)
and the PCB layout increases. Because small defects on signal trace can cause significant …
and the PCB layout increases. Because small defects on signal trace can cause significant …
Generative adversarial active learning for unsupervised outlier detection
Outlier detection is an important topic in machine learning and has been used in a wide
range of applications. In this paper, we approach outlier detection as a binary-classification …
range of applications. In this paper, we approach outlier detection as a binary-classification …
Neural transformation learning for deep anomaly detection beyond images
Data transformations (eg rotations, reflections, and cropping) play an important role in self-
supervised learning. Typically, images are transformed into different views, and neural …
supervised learning. Typically, images are transformed into different views, and neural …
Towards visually explaining variational autoencoders
Abstract Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model predictions. In …
have led to impressive progress in visualizing and understanding model predictions. In …
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
O Serradilla, E Zugasti, J Rodriguez, U Zurutuza - Applied Intelligence, 2022 - Springer
Given the growing amount of industrial data in the 4th industrial revolution, deep learning
solutions have become popular for predictive maintenance (PdM) tasks, which involve …
solutions have become popular for predictive maintenance (PdM) tasks, which involve …
Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …
However, due to the increasing complexity of CPSs and more sophisticated attacks …
On the effectiveness of image rotation for open set domain adaptation
Abstract Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled
source domain and an unlabeled target domain, while also rejecting target classes that are …
source domain and an unlabeled target domain, while also rejecting target classes that are …
efraudcom: An e-commerce fraud detection system via competitive graph neural networks
With the development of e-commerce, fraud behaviors have been becoming one of the
biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking …
biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking …