Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …

Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label
classification using partially observed labels per image, is becoming increasingly important …

Multi-label classification aims to learn classification models from instances associated with
multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels …

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse
visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) …

Learning with noisy labels (LNL) aims to ensure model generalization given a label-
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …

Classification models categorize objects into given classes, guided by training samples with
input features and labels. In practice, however, labels can be corrupted by human error or …

Noisy Labels are commonly present in data sets automatically collected from the internet,
mislabeled by non-specialist annotators, or even specialists in a challenging task, such as in …

Data decentralization and privacy constraints in federated learning systems withhold user
data from the server. As a result, intruders can take advantage of this privacy feature by …

In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an
important role in building statistically consistent classifiers. Traditionally, the transition from …