# Progressive Representative Labeling for Deep Semi-Supervised Learning Source: [Progressive Representative Labeling for Deep Semi-Supervised Learning]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:49.393Z Authors: Xiaopeng Yan, Riquan Chen, Litong Feng, Jingkang Yang, Huabin Zheng, Wayne Zhang Published: 2021-08-13T05:39:50.000Z ## Abstract Deep semi-supervised learning (SSL) has experienced significant attention in recent years, to leverage a huge amount of unlabeled data to improve the performance of deep learning with limited labeled data. Pseudo-labeling is a popular approach to expand the labeled dataset. However, whether there is a more effective way of labeling remains an open problem. In this paper, we propose to label only the most representative samples to expand the labeled set. Representative samples, selected by indegree of corresponding nodes on a directed k-nearest neighbor (kNN) graph, lie in the k-nearest neighborhood of many other samples. We design a graph neural network (GNN) labeler to label them in a progressive learning manner. Aided by the progressive GNN labeler, our deep SSL approach outperforms state-of-the-art methods on several popular SSL benchmarks including CIFAR-10, SVHN, and ILSVRC-2012. Notably, we achieve 72.1% top-1 accuracy, surpassing the previous best result by 3.3%, on the challenging ImageNet benchmark with only $10\\%$ labeled data. ## 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]()