You are here

Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning


Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, Kevin McGuinness

Publication Type: 
Refereed Conference Meeting Proceeding
Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples. We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions. We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it. The proposed approach achieves state-of-the-art results in CIFAR-10/100, SVHN, and Mini-ImageNet despite being much simpler than other methods. These results demonstrate that pseudo-labeling alone can outperform consistency regularization methods, while the opposite was supposed in previous work.
Conference Name: 
International Joint Conference on Neural Networks
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
United Kingdom (excluding Northern Ireland)
Dublin City University (DCU)
Open access repository: 
Publication document: