109 Open source projects that are alternatives of or similar to Graph Representation Learning

A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

Code for paper "Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation"

Algorithms for outlier and adversarial instance detection, concept drift and metrics.

Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution

NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

Implement of DiGCN, NeurIPS-2020

Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019

Federated Learning Library: https://fedml.ai

Pseudo-Label: Semi-Supervised Learning on CIFAR-10 in Keras

Pytorch implementation of Virtual Adversarial Training

Code for the AAAI 2018 publication "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition"

Unsupervised Data Augmentation (UDA)

Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

A full pipeline AutoML tool for tabular data

PyTorch implementation of Adversarially Learned Inference (BiGAN).

Official code for "Self-Supervised driven Consistency Training for Annotation Efficient Histopathology Image Analysis" Published in Medical Image Analysis (MedIA) Journal, Oct, 2021.

Code for Adversarial Training Methods for Semi-Supervised Text Classification

90%+ with 40 labels. please see the readme for details.

😔 😞 😣 😖 😩 Detect depression on social media using the ssToT method introduced in our ASONAM 2017 paper titled "Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media"

Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

[CVPR21] Implementation of our work "Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation"

Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]

Implementations of various VAE-based semi-supervised and generative models in PyTorch

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Pytorch implementation of "Graph Convolutional Networks for Text Classification"

📜 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition

Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition

Semi-supervised Medical Image Segmentation through Dual-task Consistency

Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"

Implementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Protein-compound affinity prediction through unified RNN-CNN

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

VOSK Speech Recognition Toolkit

Learning to Cluster. A deep clustering strategy.

A state-of-the-art semi-supervised method for image recognition

code for our TPAMI 2021 paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)

Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

Pytorch implementation of Conditional Image Synthesis with Auxiliary Classifier GANs

An implementation for our paper: CsiGAN: Robust Channel State Information-based Activity Recognition with GANs (IEEE Internet of Things Journal, 2019), which is the semi-supervised Generative Adversarial Network (GAN) for Channel State Information (CSI) -based activity recognition.

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch

noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

Code for reproducing ICT ( published in IJCAI 2019)

SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)

A curated collection of adversarial attack and defense on graph data.

[CVPR 2020] Semi-Supervised Semantic Segmentation with Cross-Consistency Training.

Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

speech to text with self-supervised learning based on wav2vec 2.0 framework

Implementation of Ladder Network in PyTorch.