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 …

Detecting out-of-distribution inputs is critical for the safe deployment of machine learning
models in the real world. However, neural networks are known to suffer from the …

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 …

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental
requirement in high-stakes applications. Unfortunately, modern deep neural networks are …

Deep neural networks usually perform poorly when the training dataset suffers from extreme
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …

Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because
models can easily overfit them. There are many types of label noise, such as symmetric …

The label transition matrix has emerged as a widely accepted method for mitigating label
noise in machine learning. In recent years, numerous studies have centered on leveraging …

Unsupervised out-of-distribution (OOD) detection is essential for the reliability of machine
learning. In the literature, existing work has shown that higher-level semantics captured by …

The rawly collected training data often comes with separate noisy labels collected from
multiple imperfect annotators (eg, via crowdsourcing). A typical way of using these separate …

Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few
active learning studies have attempted to deal with this open-set noise for sample selection …