ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning

Reducing the amount of labels required to trainconvolutional neural networks without performance degradationis key to effectively reduce human annotation effort. We pro-pose Reliable Label Bootstrapping (ReLaB), an unsupervisedpreprossessing algorithm that paves the way for semi-supervisedlearning solutions, enabling them to work with much lowersupervision. Given a dataset with few labeled samples, we firstexploit a self-supervised learning algorithm to learn unsupervisedlatent features and then apply a label propagation algorithm onthese features and select only correctly labeled samples using alabel noise detection algorithm. This enables ReLaB to createa reliable extended labeled set from the initially few labeledsamples that can then be used for semi-supervised learning.We show that the selection of the network architecture andthe self-supervised method are important to achieve successfullabel propagation and demonstrate that ReLaB substantiallyimproves semi-supervised learning in scenarios of very lim-ited supervision in CIFAR-10, CIFAR-100, and mini-ImageNet. Code: https://github.com/PaulAlbert31/ReLaB.