Stars
This is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测)
Decoupling common and unique representations for multimodal self-supervised learning
The source codes of Meta-learning for few-shot cross-domain fault diagnosis.
The PyTorch version for Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.
A collection of datasets for RUL estimation as Lightning Data Modules.
Code for "Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization" (ICASSP 2021)
智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）
Notes about courses Dive into Deep Learning by Mu Li
( TPAMI2022 / CVPR2019 Oral ) Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.
Few-shot Transfer Learning for Intelligent Fault Diagnosis of Machine
Pytorch implementation of the paper: "Conditional Adversarial Domain Generalization With a Single Discriminator for Bearing Fault Diagnosis"
Bearing fault diagnosis model based on MCNN-LSTM
Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.