liguge
Welcome to send me e-mails to communicate! Research interests include transfer learning in intelligent fault diagnosis and so forth.
Beijing Jiaotong UniversityBeijing, China
Pinned Repositories
1D-Grad-CAM-for-interpretable-intelligent-fault-diagnosis
智能故障诊断中一维类梯度激活映射可视化展示 1D-Grad-CAM for interpretable intelligent fault diagnosis
Deep-discriminative-transfer-learning-network-for-cross-machine-fault-diagnosis
Deep discriminative transfer learning network for cross-machine fault diagnosis
Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN-
Deep Residual Shrinkage Networks for Intelligent Fault Diagnosis(pytorch) 深度残差收缩网络应用于故障诊断
DLWCB
基于Laplace小波卷积和BiGRU的少量样本故障诊断方法 (Small sample fault diagnosis based on Laplace wavelet convolution and BiGRU)
EWSNet
Physics-informed Interpretable Wavelet Weight Initialization and Balanced Dynamic Adaptive Threshold for Intelligent Fault Diagnosis of Rolling Bearings pytorch
Fault-diagnosis-for-small-samples-based-on-attention-mechanism
基于注意力机制的少量样本故障诊断 pytorch
Journals-of-Prognostics-and-Health-Management
智能故障诊断和寿命预测期刊(Journals of Intelligent Fault Diagnosis and Remaining Useful Life)
MDPS_pytorch
A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling (pytorch)
PyDSN
Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations
WIDAN
Interpretable Physics-informed Domain Adaptation Paradigm for Cross-machine Transfer Fault Diagnosis (故障诊断)
liguge's Repositories
liguge/convlstmgru
Pytorch implementations of ConvLSTM and ConvGRU modules with examples
liguge/ConvLSTM-Pytorch
Implementation of ConvLSTM in pytorch applied for BCI (Brain Machine Interface) following paper: Convolutional LSTM Network-A Machine Learning Approach for Precipitation Nowcasting
liguge/CrossDomainFaultDiagnosis
Repository containing the code for the experiments and examples of my Bachelor Thesis: Cross Domain Fault Detection through Optimal Transport
liguge/DAGCN
This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.
liguge/PyTorch_Tutorial
《Pytorch模型训练实用教程》中配套代码
liguge/adlist
liguge/Attention-calibration-NMT
liguge/Code
liguge/DG_via_ER
Domain Generalization via Entropy Regularization, NeurIPS'20
liguge/dwt-domain-adaptation
Code for paper "Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss" (CVPR 2019)
liguge/FRAN
This repo provides source code for cross-domain machine fault diagnosis using an unsupervised domain adaptation approach (Feature Representation Alignment Networks).
liguge/GraphRBM
Graph regularized Restricted Boltzmann Machine, IEEE TNNLS'2018
liguge/Hands-On-One-shot-Learning-with-Python
Hands-On One-shot Learning with Python, published by Packt
liguge/MLNT
Meta-Learning based Noise-Tolerant Training
liguge/MultiClassDA
TPAMI2020 "Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice"
liguge/NLP-Conferences-Code
NLP-Conferences-Code (ACL、EMNL、NAACL、COLING、AAAI、IJCAI)
liguge/pCDBN
Private Convolutional Deep Belief Network w/Pytorch
liguge/PCS-FUDA
Official implementation of PCS in essay "Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation"
liguge/PhyloReg
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites
liguge/pikachusocute-DCLIP-RobustDA
liguge/Pysembles
liguge/pytorch-deepglr
A pytorch implementation of Deep Graph Laplacian Regularization for image denoising
liguge/pywt
PyWavelets - Wavelet Transforms in Python
liguge/ReLTanh
liguge/SAE
code for Sinkhorn Autoencoder
liguge/SincNet
SincNet is a neural architecture for efficiently processing raw audio samples.
liguge/TCN
Sequence modeling benchmarks and temporal convolutional networks
liguge/Transfer_Learning_Bearing_Fault
liguge/WaveletFCNN
Source code and dataset for the paper ""
liguge/WaveletKernelNet
This is the code for WaveletKernelNet.