Pushing the limit of semi-supervised learning with the Unified Semi-supervised Learning Benchmark - Microsoft Research

Neural models give competitive results when trained with supervised learning using sufficient high-quality labeled data. For example, according to statistics from the Paperswithcode website, recent traditional supervised learning methods can achieve an accuracy of over 88% on the ImageNet dataset, which contains millions of data. However, acquiring large amounts of labeled data is often time-consuming […]

Beginner Guide of Semi-Supervised Learning

Review — Billion-Scale Semi-Supervised Learning for Image Classification, by Sik-Ho Tsang

Remote Sensing, Free Full-Text

Certainty driven consistency loss on multi-teacher networks for semi-supervised learning - ScienceDirect

Semi-supervised incremental learning with few examples for discovering medical association rules, BMC Medical Informatics and Decision Making

Semi-supervised and semi-weakly supervised ImageNet Models

Full Guide to Contrastive Learning

Semi-supervised Semantic Segmentation via Strong-Weak Dual-Branch Network

Flexible multi-view semi-supervised learning with unified graph - ScienceDirect

Semi-supervised learning based framework for urban level building electricity consumption prediction - ScienceDirect

Pre-Trained Language Models and Their Applications - ScienceDirect

Advancing Semi-supervised Learning with Unsupervised Data Augmentation – Google Research Blog

A unified deep semi-supervised graph learning scheme based on nodes re-weighting and manifold regularization - ScienceDirect

What Is Machine Learning & Why Does It Matter? - WEKA

A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images - ScienceDirect