Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

The development of accurate methods for multi-label scene classification (MLC) of remote
sensing (RS) images is one of the most important research topics in RS. To address MLC …

The cross-modal retrieval task has raised continuous attention in recent years with the
increasing scale of multi-modal data, which has broad application prospects including …

Conventional de-noising methods rely on the assumption that all samples are independent
and identically distributed, so the resultant classifier, though disturbed by noise, can still …

In partial multi-label learning (PML), each training example is associated with a set of
candidate labels, among which only some labels are valid. As a common strategy to tackle …

In most supervised classification and regression tasks, one would assume the provided
labels reflect the ground truth. In reality, this assumption is often violated; see Cheng et …

Learning on noisy datasets is a challenging problem when pre-trained language models are
applied to real-world text classification tasks. In numerous industrial applications, acquiring …

Recently, some statistical topic modeling approaches have been widely applied in the field
of supervised document classification. However, there are few researches on these …

Multi-task classification improves generalization performance via exploiting the correlations
between tasks. However, most multi-task learning methods fail to recognize and filter noisy …

The remote sensing image analysis, classification, and pattern recognition processes all
depend on image segmentation. In this research, a search-based convolutional neural …