Introduction An anomaly is anything that deviates from the norm. Anomaly detection refers to the task of finding the anomalous instances. Defect detection is a special case of anomaly detection and has applications in industrial settings. Manual inspection by humans is still the norm in most of th...
Anomaly detectionDefect inspectionAutoencodersMachine visionIn this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) ...
This paper proposes to use autoencoders with nonlinear dimensionality reduction in the anomaly detection task. The authors apply dimensionality reduction by using an autoencoder onto both artificial data and real data, and compare it with linear PCA and kernel PCA to clarify its property. The artif...
This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. The architectural configurations under consideration encompass the original VAE baselin...
Anomaly detection and facial recognition:Autoencoders can detect anomalies, fraud or other defects—and, conversely, confirm a genuine match—by determining the reconstruction loss of examined data relative to the “normal” or “genuine” example it’s compared against. ...
These form the basis of the estimation sample used in this paper. Deviations from normal or usual patterns could indicate the emergence or development of impending financial, operational, or other risks. An anomaly detection tool might prove effective at uncovering such instances in light of ...
Anomaly Detection 36 5.03% Self-Supervised Learning 27 3.78% Denoising 27 3.78% Image Generation 22 3.08% Dimensionality Reduction 19 2.66% Quantization 16 2.24% Semantic Segmentation 16 2.24% Diversity 14 1.96%Usage Over Time Proportion of Papers (Quarterly)AutoEncoderGANVAECycleGANStyleGANStyleGAN2Jan...
In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection ...
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.References (70) ErfaniS.M. et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep ...
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning GAN Ba… 阅读全文 神经网络冻结后半部分的参数是否可行? LindgeWAI 天津大学 软件工程硕士 当我们训练深层神经网络时,反向传播算法通过以下步骤更新参数:前向传播:输入数据通过网络的各层,计算输出和损失函数。损失计算:计算预测值和实际...