Search Results for author:
Found 8 papers, 5 papers with code
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class Classification
We generalize the center-based method to unknown classes and optimize this objective based on the prior existing in the pre-trained network, i. e., pre-trained features that belong to the same class are adjacent.
A Novel Unsupervised Graph Wavelet Autoencoder for Mechanical System Fault Detection
To overcome these limitations, we propose two graph neural network models: the graph wavelet autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE).
Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction
In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets: the single-blade dataset and the video anomaly detection dataset of blades.
Ranked #1 on
Anomaly Detection
on AeBAD-V
Filter-informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis
Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches.
Superpixel Perception Graph Neural Network for Intelligent Defect Detection
First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations.
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study
Second, we integrate the whole evaluation codes into a code library and release this code library to the public for better development of this field.
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem.
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN.
