The proposed method works by projecting the in-domain samples as a union of 1-dimensional subspaces. Due to the compact feature representation of in-domain ...

ABSTRACT. Distributional mismatch between training and test data may cause the remote sensing models to behave in unpredictable.

The proposed method works by projecting the in-domain samples as a union of 1-dimensional subspaces. Due to the compact feature representation of in-domain ...

2024/04/30 · Abstract—Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting.

2024/05/20 · feature representation to detect OOD instances in. NLP. The model jointly optimizes both global and local representations using a margin-based ...

2021/04/18 · In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose ...

Our method does not require access to OOD samples and harnesses information available in all model layers by lever- aging principled anomaly detection tools.

Summary: This paper addresses the problem of generative models, specifically VAEs, when applied to OOD detection tasks. It is based upon the observation that ...

Unsupervised Anomaly detection (AD) requires build- ing a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, ...