Stars
Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
Pairwise Adversarial Training for Class-imbalanced Domain Adaptation
Code release for Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering (TPAMI 2022).
Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)
TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
Single-Source Domain Generalization for Bearing Fault Diagnosis Using Feature-Augmented Adaptive Neuro-Fuzzy Inference System
Code for our paper "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.
domain adaption with LSGAN for bearing fault diagnosis
A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Latex code for making neural networks diagrams
Pytorch implementation of Fast Adaptive Meta-Learning for Few-Shot Image Generation (FAML).
分享的所有数据集均为开源的PHM（Prognostics and Health Management）数据，涵盖故障诊断、健康评估和寿命预测等领域。
This is the implementation of Federated Meta-Learning for Few shot Fault Diagnosis (FedMeta-FFD).
meta learning for fault diagnosis
Source codes for the paper "Few-shot bearing fault diagnosis based on meta-learning with discriminant space optimization" which published in MST
A Benchmark to Study Negative Transfer https://ieeexplore.ieee.org/document/9938381
The implementation of our paper 'A Unified Label Noise-Tolerant Framework of Deep Learning-based Fault Diagnosis via A Bounded Neural Network'
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021