269 research outputs found
Deep Industrial Image Anomaly Detection: A Survey
The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metrics and datasets. In addition, we extract the new setting
from industrial manufacturing and review the current IAD approaches under our
proposed our new setting. Moreover, we highlight several opening challenges for
image anomaly detection. The merits and downsides of representative network
architectures under varying supervision are discussed. Finally, we summarize
the research findings and point out future research directions. More resources
are available at
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
This paper proposes a low-cost and highly accurate ECG-monitoring system
intended for personalized early arrhythmia detection for wearable mobile
sensors. Earlier supervised approaches for personalized ECG monitoring require
both abnormal and normal heartbeats for the training of the dedicated
classifier. However, in a real-world scenario where the personalized algorithm
is embedded in a wearable device, such training data is not available for
healthy people with no cardiac disorder history. In this study, (i) we propose
a null space analysis on the healthy signal space obtained via sparse
dictionary learning, and investigate how a simple null space projection or
alternatively regularized least squares-based classification methods can reduce
the computational complexity, without sacrificing the detection accuracy, when
compared to sparse representation-based classification. (ii) Then we introduce
a sparse representation-based domain adaptation technique in order to project
other existing users' abnormal and normal signals onto the new user's signal
space, enabling us to train the dedicated classifier without having any
abnormal heartbeat of the new user. Therefore, zero-shot learning can be
achieved without the need for synthetic abnormal heartbeat generation. An
extensive set of experiments performed on the benchmark MIT-BIH ECG dataset
shows that when this domain adaptation-based training data generator is used
with a simple 1-D CNN classifier, the method outperforms the prior work by a
significant margin. (iii) Then, by combining (i) and (ii), we propose an
ensemble classifier that further improves the performance. This approach for
zero-shot arrhythmia detection achieves an average accuracy level of 98.2% and
an F1-Score of 92.8%. Finally, a personalized energy-efficient ECG monitoring
scheme is proposed using the above-mentioned innovations.Comment: Software implementation: https://github.com/MertDuman/Zero-Shot-EC
- …