ljxstc

ljxstc's Stars

• xmu-xiaoma666/External-Attention-pytorch

  🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

  Language:Python11.8k102832k

• milesial/Pytorch-UNet

  PyTorch implementation of the U-Net for image semantic segmentation with high quality images

  Language:Python9.8k704542.6k

• MIC-DKFZ/nnUNet

  Language:Python6.4k772.2k1.9k

• MrGiovanni/UNetPlusPlus

  [IEEE TMI] Official Implementation for UNet++

  Language:Python2.4k4581547

• lucidrains/tab-transformer-pytorch

  Implementation of TabTransformer, attention network for tabular data, in Pytorch

  Language:Python8861717108

• lhyfst/knowledge-distillation-papers

  knowledge distillation papers

  75237485

• locuslab/deq

  [NeurIPS'19] Deep Equilibrium Models

  Language:Python741213081

• Andy-zhujunwen/UNET-ZOO

  including unet,unet++,attention-unet,r2unet,cenet,segnet ,fcn.

  Language:Python735425135

• pkumivision/FFC

  This is an official pytorch implementation of Fast Fourier Convolution.

  Language:Python36441332

• lukemelas/deep-spectral-segmentation

  [CVPR 2022] Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

  Language:Python23571942

• liguge/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

  Language:Python2172513

• AliMorty/Markov-Random-Field-Project

  This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.

  Language:Jupyter Notebook1194232

• asdvfghg/QCNN_for_bearing_diagnosis

  Language:Python87306

• yangluo7/CAME

  The official implementation of "CAME: Confidence-guided Adaptive Memory Optimization"

  Language:Python87245

• YMLZS/Transformer_BearingFaultDiagnosis

  Using transformer to realize Bearing Fault Diagnosis

  Language:Python60262

• patrick-kidger/generalised_shapelets

  Code for "Generalised Interpretable Shapelets for Irregular Time Series"

  Language:Jupyter Notebook57719

• aresmiki/A-case-of-the-intelligent-bearing-fault-diagnosis

  This is a case of bearing fault intelligent diagnosis. The program is written in MATLAB. The main techniques used are feature detection and neural network. This code comes from the undergraduate course assignment. The code has been written for 8 years. Because many students need such a simple case study, so it was sent out

  Language:HTML524118

• benibaeumle/Learning-Shapelets

  A PyTorch implementation of learning shapelets from the paper Grabocka et al., „Learning Time-Series Shapelets“.

  Language:Jupyter Notebook52128

• jianzhang96/MSD

  mobile phone screen surface defect segmentation (detection) dataset.

  52234

• SSTGroup/Cyclostationary-Signal-Processing

  Language:MATLAB287010

• linmh0130/CyclicSpectrumPlot

  Provide MATLAB functions to plot Cyclic Spectrum.

  Language:MATLAB19207

• fonderxu/Bearing-fault-diagnosis-datasets

  This repository currently provides common and public avaiable bearing vibration datasets, including downloading, preprocessing and loading.

  Language:Python17121

• qianlima-lab/ADSN

  The paper "Adversarial Dynamic Shapelet Network"

  Language:Python13211

• efsierraa/PyCycloVarREB

  Cyclostationary analysis in angular domain for bearing fault identification

  Language:Python12004

• fyancy/ProMo

  A Bayesian Probabilistic Framework for Mechanical Fault Diagnosis

  Language:Python11110

• Gnomeek/PolyU_report_Latex_template_library

  Language:TeX10202

• Juntongkuki/Hyper-Compression

  Language:Python9100

• Jintao-Huang/torch_tf2_study

  努力学习深度学习(已废弃), 见仓库ml_alg

  Language:Python6101

• FangBo-0219/MNAD

  Minimum Noise Amplitude Deconvolution

  2100

• Nicolasnull/using-a-cnn-to-classify-cyclostationary-data

  Language:Jupyter Notebook1201