# Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes and Model Accuracy Source: [Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes and Model Accuracy]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:37:37.790Z Authors: Robert Dupre, Jiri Fajtl, Vasileios Argyriou, Paolo Remagnin Published: 2019-06-06T21:20:12.000Z ## Abstract A novel semi-supervised learning technique is introduced based on a simple iterative learning cycle together with learned thresholding techniques and an ensemble decision support system. State-of-the-art model performance and increased training data volume are demonstrated, through the use of unlabelled data when training deeply learned classification models. Evaluation of the proposed approach is performed on commonly used datasets when evaluating semi-supervised learning techniques as well as a number of more challenging image classification datasets (CIFAR-100 and a 200 class subset of ImageNet). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()