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 …

Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

In this paper, we study the semi-supervised semantic segmentation problem via exploring
both labeled data and extra unlabeled data. We propose a novel consistency regularization …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

The recent research in semi-supervised learning (SSL) is mostly dominated by consistency
regularization based methods which achieve strong performance. However, they heavily …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Abstract We present Meta Pseudo Labels, a semi-supervised learning method that achieves
a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the …

Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …

Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …