Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

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

机器学习技术成功地应用于计算机视觉, 自然语言处理和语音识别等众多领域. 然而,
现有的大多数机器学习模型在部署后类别和参数是固定的, 只能泛化到训练集中出现的类别 …

Abstract Generalized Category Discovery (GCD) is a pragmatic and challenging open-world
task which endeavors to cluster unlabeled samples from both novel and old classes …

Proper confidence calibration of deep neural networks is essential for reliable predictions in
safety-critical tasks. Miscalibration can lead to model over-confidence and/or under …

This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …

Classifying patterns of known classes and rejecting ambiguous and novel (also called as out-
of-distribution (OOD)) inputs are involved in open world pattern recognition. Deep neural …

Deep neural networks are known to be overconfident for what they don't know in the wild
which is undesirable for decision-making in high-stakes applications. Despite quantities of …

Reliably detecting when a deployed machine learning model is likely to fail on a given input
is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing …