Python Supervised-autoencoder Resources

Replication Code for 'Moving On' - Investigating Inventors' Ethnic Origins Using Supervised Learning This repository contains the code to replicate th

FaceQgen FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment This repository is based on the paper: "FaceQgen: Semi-Supervised D

SANDS This is an annonymous repository containing code and data necessary to reproduce the results published in "Semi-supervised Stance Detection of T

Safe Local Motion Planning with Self-Supervised Freespace Forecasting By Peiyun Hu, Aaron Huang, John Dolan, David Held, and Deva Ramanan Citing us Yo

libsdae - deep-Autoencoder & denoising autoencoder A simple Tensorflow based library for Deep autoencoder and denoising AE. Library follows sklearn st

MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network This repository is the official implementation of MatchGAN: A S

Adversarial Autoencoders (AAE) Tensorflow implementation of Adversarial Autoencoders (ICLR 2016) Similar to variational autoencoder (VAE), AAE imposes

autoencoder Implementation of the Reducing the Dimensionality of Data with Neural Network – G. E. Hinton and R. R. Salakhutdinov paper. Notes Aim to m

Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation This is the official implementation for the method described in Ch

Documentation pythae This library implements some of the most common (Variational) Autoencoder models. In particular it provides the possibility to pe

Hybrid-Supervised Object Detection System Object detection system trained by hybrid-supervision/weakly semi-supervision (HSOD/WSSOD): This project is

GANs in Action by Jakub Langr and Vladimir Bok List of available code: Chapter 2: Colab, Notebook Chapter 3: Notebook Chapter 4: Notebook Chapter 6: C

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Kaggle-titanic This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this reposito

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

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

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

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,

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

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

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

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

CutPaste CutPaste: image from paper Unofficial implementation of Google's "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization"

模式识别大作业——人脸检测与识别平台 本项目是一个简易的人脸检测识别平台，提供了人脸信息录入和人脸识别的功能。前端采用 html+css+js，后端采用 pytorch，

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization Authors: Fan-yun Sun, Jordan Hoffm

SUGAR Code for "SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism" Overview train.py: the cor

SelfTask-GNN A PyTorch implementation of "Self-supervised Learning on Graphs: Deep Insights and New Directions". [paper] In this paper, we first deepe

GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN is a pre-training framework to initialize GNNs by generative pre-training. It can be

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs This repository is the implementation of SELAR. Dasol Hwang* , Jinyoung Pa

deeper_insights_into_GCNs Deeper insights into graph convolutional networks for semi-supervised learning References data and utils.py come from Implem

Reference Code for AAAI-20 paper "Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels" Please refer to htt

Self-supervised edge features for improved Graph Neural Network training Data availability: Here is a link to the raw data for the organoids dataset.

AspDecSSCL A Self-Supervised Contrastive Learning Framework for Aspect Detection This repository is a pytorch implementation for the following AAAI'21

Autoregressive Predictive Coding This repository contains the official implementation (in PyTorch) of Autoregressive Predictive Coding (APC) proposed

Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

Synthetic dataset rendering Framework for producing the synthetic datasets used in: How Useful is Self-Supervised Pretraining for Visual Tasks? Alejan

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification This repository contains the reference source code and pre-trained models (

Learning to Classify Images without Labels This repo contains the Pytorch implementation of our paper: SCAN: Learning to Classify Images without Label

Official PyTorch implementation of the paper: "Self-Supervised Relational Reasoning for Representation Learning" (2020), Patacchiola, M., and Storkey,

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework Official code for paper, Self-supervised Video Representation Le

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

Video_Pace This repository contains the code for the following paper: Jiangliu Wang, Jianbo Jiao and Yunhui Liu, "Self-Supervised Video Representation

Video Representation Learning by Recognizing Temporal Transformations [Project Page] Simon Jenni, Givi Meishvili, and Paolo Favaro. In ECCV, 2020. Thi

CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE

Self-supervised Learning of Motion Capture This is code for the paper: Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki, Self-sup

SfMLearner This codebase implements the system described in the paper: Unsupervised Learning of Depth and Ego-Motion from Video Tinghui Zhou, Matthew

[Paper] [Project page] This repository contains code for the paper: Andrew Owens, Alexei A. Efros. Audio-Visual Scene Analysis with Self-Supervised Mu

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Multi-task Self-supervised Object Detection via Recycling of Bounding Box Annotations (CVPR 2019) To make better use of given limited labels, we propo

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

Hierarchical Attention Mining (HAM) for weakly-supervised abnormality localization This is the official PyTorch implementation for the HAM method. Pap

Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.

Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation Introduction WAKD is a PyTorch implementation for our ICPR-2022 pap

WeaSEL: Weakly Supervised End-to-end Learning This is a PyTorch-Lightning-based framework, based on our End-to-End Weak Supervision paper (NeurIPS 202

Paper Khoi Nguyen, Sinisa Todorovic "A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation", accepted to ICCV 2021 Our code is mai

Reinforcement Learning via Supervised Learning Installation Run pip install -e . in an environment with Python = 3.7.0, 3.9. The code depends on MuJ

DMT: Dynamic Mutual Training for Semi-Supervised Learning This repository contains the code for our paper DMT: Dynamic Mutual Training for Semi-Superv