Unsupervised Data Augmentation (UDA)

Find label errors in datasets, weak supervision, and learning with noisy labels.

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

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

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

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

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

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

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

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

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

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

Learning to Cluster. A deep clustering strategy.

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

Tensorflow implementation of Adversarial Autoencoders

Triple-GAN: a unified framework for classification and class-conditional generation in semi-supervised learing

Semi-supervised GAN in "Improved Techniques for Training GANs"

Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)

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

PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch

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

Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

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

VOSK Speech Recognition Toolkit

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

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

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.