The learning of the deep networks largely relies on the data with human-annotated labels. In
some label insufficient situations, the performance degrades on the decision boundary with …

In semi-supervised domain adaptation, a few labeled samples per class in the target domain
guide features of the remaining target samples to aggregate around them. However, the …

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue
systems. However, the inevitable errors from automatic speech recognition (ASR) usually …

Semi-supervised learning is a promising method for medical image segmentation under
limited annotation. However, the model cognitive bias impairs the segmentation …

Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

The success of deep convolutional neural networks is partially attributed to the massive
amount of annotated training data. However, in practice, medical data annotations are …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain
distributions, with a small number of target labels available, achieving better classification …

The coronavirus disease 2019 (COVID-19) has become a severe worldwide health
emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed …

Semi-supervised learning frameworks usually adopt mutual learning approaches with
multiple submodels to learn from different perspectives. Usually, a high threshold is used to …