canghaiwuya

AGNN

Code for paper "Attentive Graph Neural Networks for Few-shot Learning"

CapsGNN

胶囊图神经网络

MABN

wdcnn

实现的是WDCNN的pytorch版本代码，对应论文的第三章 data包含了四个数据文件夹，这里只使用了0HP文件夹中的数据，里面包含了正常、内圈、外圈、滚动体共10种状态 preprocess.py的功能是对数据进行采样、编码，虽然划分出来了验证集但是并没有使用 train.py定义了用于模型训练以及显示的函数和类 main.py定义了网络模型，调用另外两个py文件获取数据和进行训练 直接运行main.py文件即可得到的结果

CGDM

Codes for "Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation" in CVPR 2021

Limited-Data-Rolling-Bearing-Fault-Diagnosis-with-Few-shot-Learning

This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning

MCD_DA

最大识别器差异度量算法

SMU_pytorch

A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

CQUThesis

:pencil: 重庆大学毕业论文LaTeX模板---LaTeX Thesis Template for Chongqing University

STGAT

时空异常检测

MMFT

Multi-source Manifold Feature Transfer

Deep-Unsupervised-Domain-Adaptation

Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31.

ILA-DA

CDAN

Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

acon

Official Repsoitory for "Activate or Not: Learning Customized Activation." [CVPR 2021]

WaveletKernelNet

小波核卷积

MEKT

Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces (MEKT)

JDDA-Master

Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation（AAAI-2019）

swd_pytorch

An unofficial PyTorch implementation for CVPR 2019 work "Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation"

EVT

使用极端值理论(Extreme Value Theory)实现阈值动态自动化设置

JDDA

Code for the paper "Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation" (AAAI-2019)