semisupervised-learning

OpenMMLab Detection Toolbox and Benchmark

A Unified Semi-Supervised Learning Codebase (NeurIPS'22)

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

Reference implementation of Diffusion2Vec (Complenet 2018) built on Gensim and NetworkX.

An implementation of "Community Preserving Network Embedding" (AAAI 2017)

It is a comprehensive resource hub compiling all graph papers accepted at the International Conference on Learning Representations (ICLR) in 2024.

A comprehensive paper list of deep learning for crack detection, in terms of learning paradigms, generalizability and datasets.

Code for reproducing results in GraphMix paper

An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)

Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

Inner product natural graph factorization machine used in 'GEMSEC: Graph Embedding with Self Clustering' .

TFG - Semisupervised learning and instance selection methods

The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

Code for L2ID CVPRW 2021 paper Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics

Deep Semisupervised Cross-modal Retrieval/Cross-view Recognition (IEEE TCYB 2022, PyTorch Code)

A machine learning project for classifying lung cancer using CT scan data. It employs semi-supervised learning with Label Spreading to improve classification with limited labeled data, differentiating between malignant and benign lung nodules for early detection.

Implementation of semi and self supervised learning on Imbalanced Dataset