Abstract Out-of-distribution (OOD) detection is a critical task for deploying machine learning
models in the open world. Distance-based methods have demonstrated promise, where …

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 …

Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …

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 …

Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier
of p (class| pixel feature). Though straightforward, this de facto paradigm neglects the …

Masked autoencoding has achieved great success for self-supervised learning in the image
and language domains. However, mask based pretraining has yet to show benefits for point …

Abstract Out-of-distribution (OOD) detection is indispensable for machine learning models
deployed in the open world. Recently, the use of an auxiliary outlier dataset during training …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Previous methods commonly rely on an OOD …

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 …

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …