The field of OOD research encompasses several key areas: 🔍 OOD detection aims to identify when a model is presented with inputs that deviate from its training distribution. This allows systems …

2021年10月21日 · In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD …

Out of Distribution (OOD) Detection is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as "unseen" data, …

A Python library for Out-of-Distribution (OOD) Detection with Deep Neural Networks based on PyTorch. The library provides: Out-of-Distribution Detection Methods; Loss Functions; …

In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforemen-tioned problems, i.e., AD, ND, OSR, OOD detection, and OD. …

2024年7月31日 · In this survey, we first present a generalized OOD detection v2, encapsulating the evolution of AD, ND, OSR, OOD detection, and OD in the VLM era. Our framework reveals …

OOD detection is the task of identifying whether a test sam-ple is drawn far from the in-distribution (ID) data or not. It is at the cornerstone of various safety-critical applications, including medical …

We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation pro-duce Sensitivity-Aware …

This final OOD score is then used to determine whether x is in-distribution or not based on a predefined threshold δ.. 3.2 One-class support vector machine. In the domain of one-class …

In this study, we introduce a novel out-of-distribution (OOD) detector called ZODE, which stands for Zoo-based OOD Detection Enhancement. Our approach incorporates sample-aware model …