Search Results for author:
Found 6 papers, 4 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.
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
Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
Method: We proposed a squeeze and excite ResNet to automatically learn deep features from 12-lead ECGs, in order to identify 24 cardiac conditions.
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.
