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 system to …

Determining whether inputs are out-of-distribution (OOD) is an essential building block for
safely deploying machine learning models in the open world. However, previous methods …

Out-of-distribution (OOD) detection has received much attention lately due to its practical
importance in enhancing the safe deployment of neural networks. One of the primary …

We consider the problem of detecting out-of-distribution images in neural networks. We
propose ODIN, a simple and effective method that does not require any change to a pre-trained …

Recent success in training deep neural networks have prompted active investigation into the
features learned on their intermediate layers. Such research is difficult because it requires …

With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

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 testing …

Over the last several decades, it has become clear that epigenetic abnormalities may be
one of the hallmarks of cancer. Posttranslational modifications of histones, for example, may …

State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining.
ImageNet classification is the de facto pretraining task for these models. Yet, ImageNet …

Single-shot high-speed 3D imaging is important for reconstructions of dynamic objects. For
fringe projection profilometry (FPP), however, it is still challenging to recover accurate 3D …