Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …

In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

We investigate the generalization of semi-supervised learning (SSL) to diverse pixel-wise
tasks. Although SSL methods have achieved impressive results in image classification, the …

While semi-supervised learning (SSL) has proven to be a promising way for leveraging
unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume …

Semi-supervised semantic segmentation aims to learn a semantic segmentation model via
limited labeled images and adequate unlabeled images. The key to this task is generating …

Although unsupervised domain adaptation methods have been widely adopted across
several computer vision tasks, it is more desirable if we can exploit a few labeled data from …

The semi-supervised semantic segmentation methods utilize the unlabeled data to increase
the feature discriminative ability to alleviate the burden of the annotated data. However, the …

Semi-supervised learning (SSL) aims to avoid the need for collecting prohibitively expensive
labelled training data. Whilst demonstrating impressive performance boost, existing SSL …

This work addresses the unsupervised domain adaptation problem, especially in the case of
class labels in the target domain being only a subset of those in the source domain. Such a …