chengshuai1992

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aaai17-cdq

The implementation of AAAI-17 paper "Collective Deep Quantization of Efficient Cross-modal Retrieval"

Adaptive-Exploration-for-Unsupervised-Person-Re-Identification

code of our work : Adaptive Exploration for Unsupervised Person Re-Identification

ADE

The code of ADE

ADNet

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (CVPR 2017)

affinity-loss

Unofficial implementation of "Max-margin Class Imbalanced Learning with Gaussian Affinity"

Awesome-Realistic-Semi-Supervised-Learning

An awesome paper list of Semi-Supervised Learning under realistic settings.

GCN

Graph Convolutional Neural Network Hashing

Video-Classification

Video-Person-ReID_yunci

Video-based Person ReID Method Implementations on MARS

chengshuai1992/Awesome-Realistic-Semi-Supervised-Learning

An awesome paper list of Semi-Supervised Learning under realistic settings.

chengshuai1992/awesome-contrastive-self-supervised-learning

A comprehensive list of awesome contrastive self-supervised learning papers.

chengshuai1992/bayesian-methods-for-ml

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

chengshuai1992/CoMatch

Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

chengshuai1992/ConsistencySSL

This repository contains the code for our paper "Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization"

chengshuai1992/Contrastive-Clustering

Code for the paper "Contrastive Clustering" (AAAI 2021)

chengshuai1992/contrastive_learning

chengshuai1992/contrastive_loss

Experiments with supervised contrastive learning methods with different loss functions

chengshuai1992/datasets

chengshuai1992/DeepClustering

Methods and Implements of Deep Clustering

chengshuai1992/DeepHash-pytorch

Implementation of Some Deep Hash Algorithms, Including DPSH、DSH、DHN、HashNet、DSDH、DTSH、DFH、GreedyHash、CSQ.

chengshuai1992/DeHiB

chengshuai1992/document-style-guide

中文技术文档的写作规范

chengshuai1992/FixMatch-pytorch

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

chengshuai1992/ganspace

Discovering Interpretable GAN Controls [NeurIPS 2020]

chengshuai1992/GCC

Graph Contrastive Clustering (ICCV2021)

chengshuai1992/HSCL

chengshuai1992/LAGNH

chengshuai1992/LaplaceNet

A PyTorch Implementation of LaplaceNet:A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification

chengshuai1992/OpenSelfSup

Self-Supervised Learning Toolbox and Benchmark

chengshuai1992/Ranking-based-Instance-Selection

Ranking-based-Instance-Selection

chengshuai1992/semco

Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

chengshuai1992/siamese-triplet

Siamese and triplet networks with online pair/triplet mining in PyTorch

chengshuai1992/simmatch

chengshuai1992/SSL4MIS

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

chengshuai1992/ssl_pytorch

pretext task, constractive learning,,,

chengshuai1992/T2T

Code for the paper: "Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning"

chengshuai1992/TorchSSL

A PyTorch-based library for semi-supervised learning (NeurIPS'21)

chengshuai1992/Tricks-of-Semi-supervisedDeepLeanring-Pytorch

PseudoLabel 2013, PI model, Tempens, MeanTeacher, ICT, MixMatch

chengshuai1992/UDAStrongBaseline

Open-source stronger baseline for unsupervised or domain adaptive object re-ID.