Competing labels: a heuristic approach to pseudo-labeling in deep semi-supervised learning

Bayrak, Hamdi Burak
Semi-supervised learning is one of the dominantly utilized approaches to reduce the reliance of deep learning models on large-scale labeled data. One mostly used method of this approach is pseudo-labeling. However, pseudo-labeling, especially its originally proposed form tends to remarkably suffer from noisy training when the assigned labels are false. In order to mitigate this problem, in our work, we investigate the gradient sent to the neural network and propose a heuristic method, called competing labels. In this method, we arrange the loss function and choose the pseudo-labels in a way that the gradient the model receives contains more than one negative element. We test our method on MNIST, Fashion-MNIST, and KMNIST datasets and show that our method has a better generalization performance compared to the originally proposed pseudo-labeling method.


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Citation Formats
H. B. Bayrak, “Competing labels: a heuristic approach to pseudo-labeling in deep semi-supervised learning,” M.S. - Master of Science, Middle East Technical University, 2022.