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 …

In label-noise learning, estimating the transition matrix has attracted more and more
attention as the matrix plays an important role in building statistically consistent classifiers …

In learning with noisy labels, the sample selection approach is very popular, which regards
small-loss data as correctly labeled during training. However, losses are generated on-the …

The great success of deep learning heavily relies on increasingly larger training data, which
comes at a price of huge computational and infrastructural costs. This poses crucial …

As one of the most fundamental techniques in multimodal learning, cross-modal matching
aims to project various sensory modalities into a shared feature space. To achieve this …

The label noise transition matrix, denoting the transition probabilities from clean labels to
noisy labels, is crucial for designing statistically robust solutions. Existing estimators for …

Deep cross-modal hashing has become an essential tool for supervised multimodal search.
These models tend to be optimized with large, curated multimodal datasets, where most …

In recent years, supervised learning has been widely used in various tasks of optical remote
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …

Evaluating fairness can be challenging in practice because the sensitive attributes of data
are often inaccessible due to privacy constraints. The go-to approach that the industry …

A major gap between few-shot and many-shot learning is the data distribution empirically
oserved by the model during training. In few-shot learning, the learned model can easily …