Python Supervised-machine-learning Resources

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Image-popularity-score - A novel deep regression method for image scoring.

Medical-Insurance-Cost-Prediction-using-Machine-learning - Here in this project, I will use regression analysis to predict medical insurance cost for people in different regions, and based on several aspects like : Smoking, Number of children, BMI...etc.

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

CS_Final_Metal_surface_detection This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021. The project is based on the dataset

BreastCancerDetection - This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features are extracted using the pre-trained CNN.

Scale complex AI/ML pipelines anywhere CodeFlare is a framework to simplify the integration, scaling and acceleration of complex multi-step analytics

CS5242_2021 Neural Networks and Deep Learning, NUS CS5242, 2021 Cloud Machine #1 : Google Colab (Free GPU) Follow this Notebook installation : https:/

VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion (Interspeech

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

Multi Task Learning for 3D segmentation Perception stack of an Autonomous Drivin

Apriori Apriori is an algorithm for frequent item set mining and association rul

Kata: Clean Machine Learning From Dirty Code First, open the Kata in Google Colab (or else download it) You can clone this project and launch jupyter-

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

A Survey on Deep Learning Technique for Video Segmentation A Survey on Deep Learning Technique for Video Segmentation Wenguan Wang, Tianfei Zhou, Fati

Easily train or fine-tune SOTA computer vision models from one training repository. SuperGradients Introduction Welcome to SuperGradients, a free open

IMBENS: Class-imbalanced Ensemble Learning in Python Language: English | Chinese/中文 Links: Documentation | Gallery | PyPI | Changelog | Source | Downl

AI-generated-characters for Learning and Wellbeing Click here for the full project page. This repository contains the source code for the paper AI-gen

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

Torchuq [logo here] TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch. TorchUQ currently supports 10 representation

Data driven GPVAD Repository for the work in TASLP 2021 Voice activity detection in the wild: A data-driven approach using teacher-student training. S

[ICCV 2021] GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning This is the official implementation of our ICCV2021 paper GyroFlow. Our pres

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble This is the code for reproducing the results of the paper Uncertainty-Bas

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Bulk2Space Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space Bulk2Space is a spatial deconvolution method based on

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Mean teachers are better role models Paper ---- NIPS 2017 poster ---- NIPS 2017 spotlight slides ---- Blog post By Antti Tarvainen, Harri Valpola (The

This repository contains source code for the experiments in a paper titled Semi-Supervised Learning with Ladder Networks by A Rasmus, H Valpola, M Hon

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

Code used for the results in the paper "ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning" Getting started Prerequisites CUD

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation PseudoSeg is a simple consistency training framework for semi-supervised image semantic s

Semi-supervised semantic segmentation using CutMix and Colour Augmentation Implementations of our papers: Semi-supervised semantic segmentation needs

PixelSSL is a PyTorch-based semi-supervised learning (SSL) codebase for pixel-wise (Pixel) vision tasks. The purpose of this project is to promote the

Learning Saliency Propagation for Semi-supervised Instance Segmentation PyTorch Implementation This repository contains: the PyTorch implementation of

SEAM The implementation of Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentaion. You can also download the repos

Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CCT) Paper, Project Page This repo contains the official implementation of CVPR

CapsuleVOS This is the code for the ICCV 2019 paper CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing. Arxiv Link: https://a

USSS_ICCV19 Code for Universal Semi Supervised Semantic Segmentation accepted to ICCV 2019. Full Paper available at https://arxiv.org/abs/1811.10323.

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations The code of: Weakly Supervised Learning of Instance Segmentation with I

Adversarial Learning for Semi-supervised Semantic Segmentation This repo is the pytorch implementation of the following paper: Adversarial Learning fo

Weakly- and Semi-Supervised Panoptic Segmentation by Qizhu Li*, Anurag Arnab*, Philip H.S. Torr This repository demonstrates the weakly supervised gro

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J

Weakly Supervised Segmentation by Tensorflow. Implements semantic segmentation in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

SESS: Self-Ensembling Semi-Supervised 3D Object Detection Created by Na Zhao from National University of Singapore Introduction This repository contai

Source code for STAC: A Simple Semi-Supervised Learning Framework for Object Detection STAC is a simple yet effective SSL framework for visual object

Wetectron Wetectron is a software system that implements state-of-the-art weakly-supervised object detection algorithms. Project CVPR'20, ECCV'20 | Pa

CSD: Consistency-based Semi-supervised learning for object Detection (NeurIPS 2019) By Jisoo Jeong, Seungeui Lee, Jee-soo Kim, Nojun Kwak Installation

COResets and Data Subset selection Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose Release Notes The official PyTorch implementation of Neural View S

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdh

DeepDeform (CVPR'2020) DeepDeform is an RGB-D video dataset containing over 390,000 RGB-D frames in 400 videos, with 5,533 optical and scene flow imag

Semi-supervised Learning for Few-shot Image-to-Image Translation [paper] Abstract: In the last few years, unpaired image-to-image translation has witn

Deep SAD: A Method for Deep Semi-Supervised Anomaly Detection This repository provides a PyTorch implementation of the Deep SAD method presented in ou

Learning to Self-Train for Semi-Supervised Few-Shot Classification This repository contains the TensorFlow implementation for NeurIPS 2019 Paper "Lear

MLCR This is the source code for paper Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang

LABES This is the code for EMNLP 2020 paper "A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised L

MixText This repo contains codes for the following paper: Jiaao Chen, Zichao Yang, Diyi Yang: MixText: Linguistically-Informed Interpolation of Hidden

Semi-SDP Semi-supervised parser for semantic dependency parsing. This repo contains the code used for the semi-supervised semantic dependency parser i

High resolution disentanglement datasets This repository contains the Falcor3D and Isaac3D datasets, which present a state-of-the-art challenge for co

MarginGAN This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning". 1."preliminary" is the imp

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model Baris Gecer 1, Binod Bhattarai 1

Good Semi-Supervised Learning that Requires a Bad GAN This is the code we used in our paper Good Semi-supervised Learning that Requires a Bad GAN Ziha

Class-Attentive Diffusion Network for Semi-Supervised Classification Official Implementation of AAAI 2021 paper Class-Attentive Diffusion Network for

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs MATLAB implementation of the paper: P. Mercado, F. Tudisco, and M. Hein,

Neural Structured Learning in TensorFlow Neural Structured Learning (NSL) is a new learning paradigm to train neural networks by leveraging structured

Graph-based joint model with Nonignorable Missingness (GNM) This is a Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Lear

G3NN This repo provides a pytorch implementation for the 4 instantiations of the flexible generative framework as described in the following paper: A

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

few-shot-ssl-public Code for paper Meta-Learning for Semi-Supervised Few-Shot Classification. [arxiv] Dependencies cv2 numpy pandas python 2.7 / 3.5+

Semi-supervised Deep Kernel Learning This is the code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data

FAIR Self-Supervision Benchmark is deprecated. Please see VISSL, a ground-up rewrite of benchmark in PyTorch. FAIR Self-Supervision Benchmark This cod

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters. Overview This project is a Torch implementation for our CVPR 2016 paper

Graph Contrastive Learning with Augmentations PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix] Yuning You*

CCGL: Contrastive Cascade Graph Learning This repo provides a reference implementation of Contrastive Cascade Graph Learning (CCGL) framework as descr

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval This repo provides personal implementation of paper Approximate Ne

Beibo is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.

Change Detection Laboratory Developing and benchmarking deep learning-based remo

Zero-shot-dual-iter-LLE The code of Zero-shot learning for low-light image enhancement based on dual iteration. You can get the real night image tests

OpenSelfSup News Downstream tasks now support more methods(Mask RCNN-FPN, RetinaNet, Keypoints RCNN) and more datasets(Cityscapes). 'GaussianBlur' is

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training Original implementation for paper GCC: Graph Contrastive Coding for Graph Neural N

GRACE The official PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE). For a thorough resource collection of self-superv

GMI (Graphical Mutual Information) Graph Representation Learning via Graphical Mutual Information Maximization (Peng Z, Huang W, Luo M, et al., WWW 20

SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th

Contrastive Learning of Structured World Models This repository contains the official PyTorch implementation of: Contrastive Learning of Structured Wo

DGI Deep Graph Infomax (Veličković et al., ICLR 2019): https://arxiv.org/abs/1809.10341 Overview Here we provide an implementation of Deep Graph Infom

Unsupervised Attributed Multiplex Network Embedding (DMGI) Overview Nodes in a multiplex network are connected by multiple types of relations. However

Contrastive Multi-View Representation Learning on Graphs This work introduces a self-supervised approach based on contrastive multi-view learning to l

Subg-Con Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning (Jiao et al., ICDM 2020): https://arxiv.org/abs/2009.10273 Over

Graph-InfoClust-GIC [PAKDD 2021] PAKDD'21 version Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs Preprint version Graph InfoClu

Introduction QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and

ncnn ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployme

Deep Hedging Demo Pricing Derivatives using Machine Learning 1) Jupyter version: Run ./colab/deep_hedging_colab.ipynb on Colab. 2) Gui version: Run py

CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21 For more information, check out the paper on [arXiv]. Training with different

Awesome Model-Based Reinforcement Learning This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository w

ElegantRL “小雅”: Scalable and Elastic Deep Reinforcement Learning ElegantRL is developed for researchers and practitioners with the following advantage

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

Numerical Bric-a-Brac Collections of numerical techniques with Python and standard computational packages (Numpy, SciPy, Numba, Matplotlib ...). Diffe