Python Supervised-clustering Resources

EEND-vector clustering The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates

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

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.)

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

Simple-Kmeans-Clustering-Algorithm Abstract K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to

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

Simple Keyword Clusterer A simple machine learning package to cluster keywords in higher-level groups. Example: "Senior Frontend Engineer" -- "Fronte

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

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

Using a predicted aligned error matrix corresponding to an AlphaFold2 model , returns a series of lists of residue indices, where each list corresponds to a set of residues clustering together into a pseudo-rigid domain.

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

Framework overview This library allows to quickly implement different architectures based on Reservoir Computing (the family of approaches popularized

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

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

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation [Paper] Prerequisites To install requirements: pip install -r requirements.txt

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

DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN. Allowing for both categorical and numerical data, DenseClus makes it possible to incorporate all features in clustering.

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

JigsawClustering Jigsaw Clustering for Unsupervised Visual Representation Learning Pengguang Chen, Shu Liu, Jiaya Jia Introduction This project provid

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

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

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

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

This is the official PyTorch implementation of the ALBEF paper [Blog]. This repository supports pre-training on custom datasets, as well as finetuning on VQA, SNLI-VE, NLVR2, Image-Text Retrieval on MSCOCO and Flickr30k, and visual grounding on RefCOCO+. Pre-trained and finetuned checkpoints are released.

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

ClusterGCN ⠀⠀ A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). A

Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation This repository is the pytorch implementation of our paper: Hierarchical Cr

CLSA CLSA is a self-supervised learning methods which focused on the pattern learning from strong augmentations. Copyright (C) 2020 Xiao Wang, Guo-Jun

DeLORA: Self-supervised Deep LiDAR Odometry for Robotic Applications Overview Paper: link Video: link ICRA Presentation: link This is the correspondin

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Code accompanying CVPR'20 paper of the same title. Paper lin

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

ProSelfLC: CVPR 2021 ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks For any specific discussion or potential fu

Depth-supervised NeRF: Fewer Views and Faster Training for Free Project | Paper | YouTube Pytorch implementation of our method for learning neural rad

Self-supervised Graph-level Representation Learning with Local and Global Structure Introduction This project is an implementation of ``Self-supervise

TS-CAM: Token Semantic Coupled Attention Map for Weakly SupervisedObject Localization This is the official implementaion of paper TS-CAM: Token Semant

Knodle (Knowledge-supervised Deep Learning Framework) - a new framework for weak supervision with neural networks. It provides a modularization for se

Project page for the paper Semi-Supervised Raw-to-Raw Mapping 2021.

I-SECRET This is the implementation of the MICCAI 2021 Paper "I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive con

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering Jang Hyun Cho1, Utkarsh Mall2, Kavita Bala2, Bharath Harihar

One Thing One Click One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation (CVPR2021) Code for the paper One Thi

EgoNet Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation". This repo inclu

S2HAND: Model-based 3D Hand Reconstruction via Self-Supervised Learning S2HAND presents a self-supervised 3D hand reconstruction network that can join

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

Patch-Rotation(PatchRot) Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models Submitted to Neurips2021 To

Deep Conditional Gaussian Mixture Model for Constrained Clustering. This repository holds the code for the paper Deep Conditional Gaussian Mixture Mod

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN If you use this code for your research, please cite ou

SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks (CVPR 2021 Oral) This repository contains the official PyTorch implementation

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated [talk] [poster] [appendix] Yuning You, Tianlong C

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yan

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

Self-Classifier: Self-Supervised Classification Network Official PyTorch implementation and pretrained models of the paper Self-Supervised Classificat

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations This repo contains the Pytorch implementation of our paper: Revisit

SingleClassRL Implementation of weak single object segmentation from paper "Regularized Loss for Weakly Supervised Single Class Semantic Segmentation"

Semi Hand-Object Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time (CVPR 2021).

Semi-Supervised Domain Generalization This code is the official implementation of the following paper: Semi-Supervised Domain Generalization with Stoc

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models Codes for this paper The Lottery Tickets Hypo

SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L

Self-Supervised Reward Regression (SSRR) Codebase for CoRL 2021 paper "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression "

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

TorchSemiSeg [CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision by Xiaokang Chen1, Yuhui Yuan2, Gang Zeng1, Jingdong Wang

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

MSF Official code for "Mean Shift for Self-Supervised Learning" Requirements Python = 3.7.6 PyTorch = 1.4 torchvision = 0.5.0 faiss-gpu = 1.6.1 In

FGR This repository contains the python implementation for paper "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection"(I

Mixed supervision for surface-defect detection: from weakly to fully supervised learning [Computers in Industry 2021] Official PyTorch implementation

DIRL: Domain-Invariant Representation Learning Domain-Invariant Representation Learning (DIRL) is a novel algorithm that semantically aligns both the

DS3L This is the code for paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020. Setups The code is implem

Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

Video Person Clustering Repo for the Video Person Clustering dataset, and code for the associated paper. This reporsitory contains the Video Person Cl