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 …

Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …

Despite the remarkable accuracy of deep neural networks in object detection, they are costly
to train and scale due to supervision requirements. Particularly, learning more object …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

Semi-supervised learning (SSL) has a potential to improve the predictive performance of
machine learning models using unlabeled data. Although there has been remarkable recent …

Supervised learning based object detection frameworks demand plenty of laborious manual
annotations, which may not be practical in real applications. Semi-supervised object …

We propose a semi-supervised approach for contemporary object detectors following the
teacher-student dual model framework. Our method is featured with 1) the exponential …

Making a precise annotation in a large dataset is crucial to the performance of object
detection. While the object detection task requires a huge number of annotated samples to …

We propose an approach for unsupervised adaptation of object detectors from label-rich to
label-poor domains which can significantly reduce annotation costs associated with …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …