497 Open source projects that are alternatives of or similar to sinkhorn-label-allocation

UI for Keras to implement image classification written in python and django

Pytorch implementation of Virtual Adversarial Training

image classifier, retrained for specific classes

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

Semi-Supervised Learning and Collective Classification

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

Concise, Modular, Human-friendly PyTorch implementation of MixNet with Pre-trained Weights.

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

Real time image classification with React Native and Tensorflow lite.

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

Balanced Multiclass Image Classification with TensorFlow on Python.

PyTorch implementation of Adversarially Learned Inference (BiGAN).

Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"

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

Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification

😔 😞 😣 😖 😩 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"

Implementation of Ladder Network in PyTorch.

Python codes for weakly-supervised learning

Federated Learning Library: https://fedml.ai

Active learning for deep object detection using YOLO

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

[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

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

Pytorch implementation of pyramidnet

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

MyBB CAPTCHA Solver using Convolutional Neural Network in Keras

Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

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

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

Modernize and Extend: JPetStore on IBM Cloud Kubernetes Service

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

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

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

A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

A full pipeline AutoML tool for tabular data

See Triple-GAN-V2 in PyTorch: https://github.com/taufikxu/Triple-GAN

Implement of DiGCN, NeurIPS-2020

Image annotation tool to make Machine Learning or others stuffs

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

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

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

PyTorch implementation of shake-drop regularization

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

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

[ICLR2022] official implementation of UniFormer

SPEAR: Programmatically label and build training data quickly.

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.

Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning

PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

Semi-supervised Learning GAN

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Awesome Few-shot learning

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

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

Unsupervised Data Augmentation (UDA)

PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"

Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)

Dialogue Knowledge Transfer Networks (DiKTNet)