Python Semi-supervised-methods Resources

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021) Kun Wang, Zhenyu Zhang, Zhiqiang Yan, X

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create

Motif-based Graph Self-Supervised Learning for Molecular Property Prediction Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Se

Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning This repository is the official implementation of CARE.

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

MMSceneGraph Introduction MMSceneneGraph is an open source code hub for scene graph generation as well as supporting downstream tasks based on the sce

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision Paper | Project page | Demo (Youtube) | Demo (Bilib

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

SubTab: Author: Talip Ucar ([email protected]) The official implementation of the paper, SubTab: Subsetting Features of Tabular Data for Self-Supervis

Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

The Official PyTorch Implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Vanilla GCSL This repository contains a vanilla implementation of "Learning to Reach Goals via Iterated Supervised Learning" proposed by Dibya Gosh et

Video Autoencoder: self-supervised disentanglement of 3D structure and motion This repository contains the code (in PyTorch) for the model introduced

Sci - cpy README is a stub. Do expand it. Objective This repository is meant to be a ready reference for scientific computation methods. Do ⭐ it if yo

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

A curated (most recent) list of resources for Learning with Noisy Labels

Self-Supervised depth kalilia

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision.

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Semi-supervised-learning-for-medical-image-segmentation. Recently, semi-supervised image segmentation has become a hot topic in medical image computin

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

About subwAI subwAI - a project for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation

geoopt Manifold aware pytorch.optim. Unofficial implementation for “Riemannian Adaptive Optimization Methods” ICLR2019 and more. Installation Make sur

GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co

An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021) Introduction This is an official pytorch implemen

Deep-Leafsnap Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhev

Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge Correlation Explanation (CorEx) is a topic model that yields rich topics tha

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

The project is investigating methods to extract human-marked data from document forms such as surveys and tests. They can read questions, multiple-choice exam papers, and grade.

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds This is the official code implementation for the paper "Spatio-temporal Se

End-to-End Semi-Supervised Object Detection with Soft Teacher By Mengde Xu*, Zheng Zhang*, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai,

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

Factoral-Methods-using-two-different-method Here, I am finding the factorial of a number by using two different method. The first method is by using f

Three-different-method-for-list-reversion Here, I have discuss the three methods of list reversion. The three methods are built-in method, slicing met

mailcat The only cat who can find existing email addresses by nickname. Usage First install requirements: pip3 install -r requirements.txt Then just

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation The code of: Cross-Image Region Mining with Region Proto

solar-system Simulation of the solar system using various nummerical methods Download the repo Make shure matplotlib, scipy etc. are installed execute

Selene is a Python library and command line interface for training deep neural networks from biological sequence data such as genomes.

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Pre-trained (foundation) models across tasks (understanding, generation and translation), languages (100+ languages), and modalities (language, image, audio, vision + language, audio + language, etc.)

Case studies with Bayesian methods

End-to-end neural table-text understanding models.

✨A Python framework to explore, label, and monitor data for NLP projects

Sound event detection aims at processing the continuous acoustic signal and converting it into symbolic descriptions of the corresponding sound events present at the auditory scene.

Self-Supervised Pillar Motion Learning for Autonomous Driving Chenxu Luo, Xiaodong Yang, Alan Yuille Self-Supervised Pillar Motion Learning for Autono

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Normalization Matters in Weakly Supervised Object Localization (ICCV 2021) 99% of the code in this repository originates from this link. ICCV 2021 pap

GeDML is an easy-to-use generalized deep metric learning library

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

SSH: A Self-Supervised Framework for Image Harmonization (ICCV 2021) code for SSH Representative Examples Main Pipeline RealHM DataSet Google Drive Pr

sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

MNIST-to-SVHN and SVHN-to-MNIST PyTorch Implementation of CycleGAN and Semi-Supervised GAN for Domain Transfer. Prerequites Python 3.5 PyTorch 0.1.12

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Includes smoo

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition This is the offical website for paper ''

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Learning-Action-Completeness-from-Points Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal A

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

Flood Detection Challenge This repository contains code for our submission to the ETCI 2021 Competition on Flood Detection (Winning Solution #2). Acco

ST++ This is the official PyTorch implementation of our paper: ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation. Lihe Ya

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

💕YOLOV5 semi-automatic annotation tool (Based on labelImg)

MoCo v3 for Self-supervised ResNet and ViT Introduction This is a PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT. The original M

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

README.md shall be finished soon. WSSGG 0 Overview 1 Installation 1.1 Faster-RCNN 1.2 Language Parser 1.3 GloVe Embeddings 2 Settings 2.1 VG-GT-Graph

AD penetration Testing Project By Ruben Enkaoua - GL4Di4T0R Based on the TCM PEH course (Heath Adams) Index 1 - Setting Up the Lab Intallation of a Wi

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data arXiv This is the code base for weakly supervised NER. We provide a

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

Self-Supervised Learning with Vision Transformers By Zhenda Xie*, Yutong Lin*, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao and Han Hu This repo is the

USDAN The implementation of Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, which is accepte

Single Image Pretraining of Visual Representations As shown in the paper A critical analysis of self-supervision, or what we can learn from a single i

Video Contrastive Learning with Global Context (VCLR) This is the official PyTorch implementation of our VCLR paper. Install dependencies environments

Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada

SemCo The official pytorch implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

torch_DE_solver Combines power of torch, numerical methods and math overall to conquer and solve ALL {O,P}DEs There are three examples to provide a li

GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg

ReSSL: Relational Self-Supervised Learning with Weak Augmentation This repository contains PyTorch evaluation code, training code and pretrained model

This repo contains the official implementation of Spatial Contrastive Learning for Few-Shot Classification (SCL), which presents of a novel contrastive learning method applied to few-shot image classification in order to learn more general purpose embeddings, and facilitate the test-time adaptation to novel visual categories.

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give