Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …

Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …

The inherent challenge of multimodal fusion is to precisely capture the cross-modal
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …

Open-world classification systems should discern out-of-distribution (OOD) data whose
labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

Detecting out-of-distribution (OOD) samples are crucial for machine learning models
deployed in open-world environments. Classifier-based scores are a standard approach for …

Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

Abstract Out-of-distribution (OOD) detection is important for deploying reliable machine
learning models on real-world applications. Recent advances in outlier exposure have …