no code implementations • 4 Sep 2023 • Zilong Zhang, Zhibin Zhao, Deyu Meng, Xingwu Zhang, Xuefeng Chen
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.
no code implementations • 20 Jul 2023 • Tianfu Li, Chuang Suna, Ruqiang Yan, Xuefeng Chen, Olga Fink
To overcome these limitations, we propose two graph neural network models: the graph wavelet autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE).
2 code implementations • 5 Apr 2023 • Zilong Zhang, Zhibin Zhao, Xingwu Zhang, Chuang Sun, Xuefeng Chen
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
no code implementations • 27 Mar 2023 • Tianfu Li, Chuang Sun, Olga Fink, Yuangui Yang, Xuefeng Chen, Ruqiang Yan
Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches.
1 code implementation • 14 Oct 2022 • Hongbing Shang, Qixiu Yang, Chuang Sun, Xuefeng Chen, Ruqiang Yan
First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations.
3 code implementations • 6 Mar 2020 • Zhibin Zhao, Tianfu Li, Jingyao Wu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen
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.
1 code implementation • 28 Dec 2019 • Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen
Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem.
1 code implementation • 12 Nov 2019 • Tianfu Li, Zhibin Zhao, Chuang Sun, Li Cheng, Xuefeng Chen, Ruqiang Yan, Robert X. Gao
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.