Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Semisupervised learning (SSL) has received a lot of recent attention as it alleviates the need
for large amounts of labeled data which can often be expensive, requires expert knowledge …

Deep neural networks demonstrated their ability to provide remarkable performances on a
wide range of supervised learning tasks (eg, image classification) when trained on extensive …

The recently proposed self-ensembling methods have achieved promising results in deep
semi-supervised learning, which penalize inconsistent predictions of unlabeled data under …

Deep neural networks achieve remarkable performances on a wide range of tasks with the
aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor …

Deep neural networks have been successfully applied to many real-world applications.
However, such successes rely heavily on large amounts of labeled data that is expensive to …

Consistency regularization on label predictions becomes a fundamental technique in semi-
supervised learning, but it still requires a large number of training iterations for high …

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled
data when labels are limited or expensive to obtain. SSL algorithms based on deep neural …

We present a novel cost function for semi-supervised learning of neural networks that
encourages compact clustering of the latent space to facilitate separation. The key idea is to …

Recently, consistency-based methods have achieved state-of-the-art results in semi-
supervised learning (SSL). These methods always involve two roles, an explicit or implicit …