Steven's repositories
mmdetection
OpenMMLab Detection Toolbox and Benchmark
APN-ZSL
This is a PyTorch implementation of the paper "Attribute Prototype Network for Zero-Shot Learning".
AugmmentationCode
Matlab code for data augmentation
awesome-anomaly-detection
A curated list of awesome anomaly detection resources
awesome_OpenSetRecognition_list
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
barlowtwins
PyTorch implementation of Barlow Twins.
CPCaD-Bench
Continuous Industrial Process datasets for benchmarking Causal Discovery methods
deep-smoke-machine
Deep learning models and dataset for recognizing industrial smoke emissions
Enjoy-Hamburger
[ICLR 2021] Is Attention Better Than Matrix Decomposition?
fastRPCA
Matlab code for all variants of robust PCA and SPCP
Fault-Classification
Power System overhead transmission line Fault classification
fire-smoke-detect-yolov4
fire-smoke-detect-yolov4-yolov5 and fire-smoke-detection-dataset 火灾检测，烟雾检测
gcm-cf
[CVPR 2021] Released code for Counterfactual Zero-Shot and Open-Set Visual Recognition
geotorch
Constrained optimization toolkit for PyTorch
hamburger-pytorch
Pytorch implementation of the hamburger module from the ICLR 2021 paper "Is Attention Better Than Matrix Decomposition"
imagefusion_Infrared_visible_latlrr
infrared anf visible image fusion using latent low-rank representation
IMS-dataset-for-fault-diagnosis
IMS dataset for fault diagnosis include NA，IF，OF，BF.
Industrial_ZSL
Source Code of Industrial_ZSL in TII
interpretability-by-parts
[CVPR 2020 Oral] Interpretable and Accurate Fine-grained Recognition via Region Grouping
minisom
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
mmfewshot
OpenMMLab FewShot Learning Toolbox and Benchmark
pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
pytorch_geometric
Graph Neural Network Library for PyTorch
Salehi_submitted_2020
This repository contains the codes to reproduce the results of our proposed novelty detection algorithm based on adversarially robust autoencoder.
Semantic-consistent-Embedding
The implementation of "Semantic-consistent Embedding for Zero-shot Fault Diagnosis".