704 Repositories
Python supervised-clustering Libraries
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images (ISBI 2021, MELBA 2021)
MultiMix This repository contains the implementation of MultiMix. Our publications for this project are listed below: "MultiMix: Sparingly Supervised,
PyTorch implementation of "A Simple Baseline for Low-Budget Active Learning".
A Simple Baseline for Low-Budget Active Learning This repository is the implementation of A Simple Baseline for Low-Budget Active Learning. In this pa
Generate custom detailed survey paper with topic clustered sections and proper citations, from just a single query in just under 30 mins !!
Auto-Research A no-code utility to generate a detailed well-cited survey with topic clustered sections (draft paper format) and other interesting arti
The PyTorch implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision The PyTorch implementation of DiscoBox: Weakly Supe
NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"
[Official] FINE Samples for Learning with Noisy Labels This repository is the official implementation of "FINE Samples for Learning with Noisy Labels"
Project repo for the paper SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition
SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition (BMVC 2021) Project repo for the paper SILT: Self-supervised Lighting Trans
IEEE Winter Conference on Applications of Computer Vision 2022 Accepted
SSKT(Accepted WACV2022) Concept map Dataset Image dataset CIFAR10 (torchvision) CIFAR100 (torchvision) STL10 (torchvision) Pascal VOC (torchvision) Im
Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021
MRefG Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021 1. Requirements To reproduc
PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech
Cross-Speaker-Emotion-Transfer - PyTorch Implementation PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Conditio
This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021.
MCGC Description This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021. Datasets Results ACM DBLP IMDB Amazon photos Amazon co
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
Unsupervised Depth Completion with Calibrated Backprojection Layers PyTorch implementation of Unsupervised Depth Completion with Calibrated Backprojec
Self-supervised learning on Graph Representation Learning (node-level task)
graph_SSL Self-supervised learning on Graph Representation Learning (node-level task) How to run the code To run GRACE, sh run_GRACE.sh To run GCA, sh
This repository is the official implementation of Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (NeurIPS21).
Core-tuning This repository is the official implementation of ``Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regular
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021
DIFFNet This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised d
Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper
LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models
[NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning
SoCo [NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning By Fangyun Wei*, Yue Gao*, Zhirong Wu, Han Hu,
pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning
ABC:Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning, NeurIPS 2021 pytorch implementation of ABC : Auxiliary Balanced Class
Dynamic Bottleneck for Robust Self-Supervised Exploration
Dynamic Bottleneck Introduction This is a TensorFlow based implementation for our paper on "Dynamic Bottleneck for Robust Self-Supervised Exploration"
Official codes: Self-Supervised Learning by Estimating Twin Class Distribution
TWIST: Self-Supervised Learning by Estimating Twin Class Distributions Codes and pretrained models for TWIST: @article{wang2021self, title={Self-Sup
[BMVC 2021] Official PyTorch Implementation of Self-supervised learning of Image Scale and Orientation Estimation
Self-Supervised Learning of Image Scale and Orientation Estimation (BMVC 2021) This is the official implementation of the paper "Self-Supervised Learn
ML From Scratch
ML from Scratch MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Clustering K Nearest Neighbours Decision
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa
Project code for weakly supervised 3D object detectors using wide-baseline multi-view traffic camera data: WIBAM.
WIBAM (Work in progress) Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data 3D object dete
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.
Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P
Code for MarioNette: Self-Supervised Sprite Learning, in NeurIPS 2021
MarioNette | Webpage | Paper | Video MarioNette: Self-Supervised Sprite Learning Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Ale
Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.
Vector AI is a framework designed to make the process of building production grade vector based applications as quickly and easily as possible. Create
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.
Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with
A PyTorch-based library for semi-supervised learning
News If you want to join TorchSSL team, please e-mail Yidong Wang ([email protected]; [email protected]) for more information. We plan to add more
This is the 3D Implementation of 《Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation》
CoraNet This is the 3D Implementation of 《Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation》 Environment pytor
Demystifying How Self-Supervised Features Improve Training from Noisy Labels
Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021
DIFFNet This repo is for Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised de
TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture.
A Brain Tumor Detection and Classification Model built using RESNET50 architecture. The model is also deployed as a web application using Flask framework.
[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning
TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.
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
Codes for AAAI'21 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'
DHCN Codes for AAAI 2021 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'. Please note that the default link
Self-supervised Graph Learning for Recommendation
SGL This is our Tensorflow implementation for our SIGIR 2021 paper: Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian,and Xing
Implementation of ICCV 2021 oral paper -- A Novel Self-Supervised Learning for Gaussian Mixture Model
SS-GMM Implementation of ICCV 2021 oral paper -- Self-Supervised Image Prior Learning with GMM from a Single Noisy Image with supplementary material R
A collection of neat and practical data science and machine learning projects
Data Science A collection of neat and practical data science and machine learning projects Explore the docs » Report Bug · Request Feature Table of Co
CPC-big and k-means clustering for zero-resource speech processing
The CPC-big model and k-means checkpoints used in Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing.
Weakly-supervised Text Classification Based on Keyword Graph
Weakly-supervised Text Classification Based on Keyword Graph How to run? Download data Our dataset follows previous works. For long texts, we follow C
Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision Training Efficiency We show the training efficiency of our DSLP model b
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021. Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du. Code f
The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation This repository is the official implementation of CVPR 2021 paper:
Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision Training Efficiency We show the training efficiency of our DSLP model b
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.
Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima
[ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
When Does Self-Supervision Help Graph Convolutional Networks? PyTorch implementation for When Does Self-Supervision Help Graph Convolutional Networks?
A JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short.
BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa
Self-Supervised Speech Pre-training and Representation Learning Toolkit.
What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site
UniSpeech - Large Scale Self-Supervised Learning for Speech
UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Preprocess file of the dataset used in implicit sub-populations: (Demographic groups
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W
Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)
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)
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)
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022
CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC
The self-supervised goal reaching benchmark introduced in Discovering and Achieving Goals via World Models
Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create
Official implementation of "Motif-based Graph Self-Supervised Learning forMolecular Property Prediction"
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, AAAI 2021.
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)
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
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)
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.
3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D
The Official PyTorch Implementation of DiscoBox.
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
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning
The official implementation of the paper, "SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning"
SubTab: Author: Talip Ucar ([email protected]) The official implementation of the paper, SubTab: Subsetting Features of Tabular Data for Self-Supervis
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search
Tribuo - A Java machine learning library
Tribuo - A Java prediction library (v4.1) Tribuo is a machine learning library in Java that provides multi-class classification, regression, clusterin
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"
Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR
UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
The Official PyTorch Implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
Learning to Reach Goals via Iterated Supervised Learning
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
Video Autoencoder: self-supervised disentanglement of 3D structure and motion This repository contains the code (in PyTorch) for the model introduced
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.
An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation
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
A curated (most recent) list of resources for Learning with Noisy Labels
Self-Supervised depth kalilia
Self-Supervised depth kalilia
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa
Implementation of linear CorEx and temporal CorEx.
Correlation Explanation Methods Official implementation of linear correlation explanation (linear CorEx) and temporal correlation explanation (T-CorEx
A self-supervised 3D representation learning framework named viewpoint bottleneck.
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
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
Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch
Neural Distance Embeddings for Biological Sequences Official implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTo
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Semi-supervised-learning-for-medical-image-segmentation. Recently, semi-supervised image segmentation has become a hot topic in medical image computin
WRENCH: Weak supeRvision bENCHmark
🔧 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
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.
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.
Scripts for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation and a convolutional neural network (CNN) for image classification
About subwAI subwAI - a project for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte
The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining
The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining
Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)
Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co
[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021) Introduction This is an official pytorch implemen
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx
Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge Correlation Explanation (CorEx) is a topic model that yields rich topics tha
A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.
Multilingual Latent Dirichlet Allocation (LDA) Pipeline This project is for text clustering using the Latent Dirichlet Allocation (LDA) algorithm. It
Supervised Contrastive Learning for Downstream Optimized Sequence Representations
SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext
A self-supervised 3D representation learning framework named viewpoint bottleneck.
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
Code for the paper "Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds" (ICCV 2021)
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds This is the official code implementation for the paper "Spatio-temporal Se
Semi-Supervised Learning, Object Detection, ICCV2021
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 for Debiased Scene Representation
Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo