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 …

Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from
input data through a model. This problem has attracted increasing attention in the area of …

Abstract Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and
safety of deep neural networks in real-world scenarios. Different from most previous OOD …

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning
models in an open-world setting. However, existing OOD detection solutions can be brittle in …

Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to
the in-distribution task. The OOD detection performance when the in-distribution (ID) is …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Abstract Out-of-distribution (OOD) detection aims to identify OOD data based on
representations extracted from well-trained deep models. However, existing methods largely …

In machine learning, it is often observed that standard training outputs anomalously high
confidence for both in-distribution (ID) and out-of-distribution (OOD) data. Thus, the ability to …

Neural networks have led to major improvements in image classification but suffer from
being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution …