Modern GANs excel at generating high-quality and diverse images. However, when
transferring the pretrained GANs on small target data (eg, 10-shot), the generator tends to …

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for
decades. The primary family of SSL algorithms, known as pseudo-labeling, involves …

Consistency-based Semi-supervised learning (SSL) has achieved promising performance
recently. However, the success largely depends on the assumption that the labeled and …

The key to semi-supervised learning (SSL) is to explore adequate information to leverage
the unlabeled data. Current dominant approaches aim to generate pseudo-labels on weakly …

The high prevalence of mental disorders gradually poses a huge pressure on the public
healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to …

Ideally, visual learning algorithms should be generalizable, for dealing with any unseen
domain shift when deployed in a new target environment; and data-efficient, for reducing …

Industrial processes are developing toward intelligence and complexity, which brings
challenges to intelligent process monitoring. An effective fault diagnosis model plays a vital …

Semi-Supervised object detection (SSOD) aims to improve the generalization ability of
object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD …

Abstract Semi-Supervised Temporal Action Localization (SS-TAL) aims to improve the
generalization ability of action detectors with large-scale unlabeled videos. Albeit the recent …

The continuing increase in the incidence and recognition of children's sleep disorders has
heightened the demand for automatic pediatric sleep staging. Supervised sleep stage …