Out-of-Distribution Detection

321 papers with code • 50 benchmarks • 22 datasets

Detect out-of-distribution or anomalous examples.

Libraries

Use these libraries to find Out-of-Distribution Detection models and implementations

Latest papers with no code

Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble

no code yet • 24 Mar 2024

Our experimental results demonstrate the superior performance of the MC Ensemble strategy in OOD detection compared to both the naive Deep Ensemble method and a standalone model of comparable size.

Hypothesis-Driven Deep Learning for Out of Distribution Detection

no code yet • 21 Mar 2024

Given a trained DNN and some input, we first feed the input through the DNN and compute an ensemble of OoD metrics, which we term latent responses.

Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model

no code yet • 20 Mar 2024

Out-of-distribution (OOD) detection is a critical task to ensure the reliability and security of machine learning models deployed in real-world applications.

Variational Sampling of Temporal Trajectories

no code yet • 18 Mar 2024

In this work, we introduce a mechanism to learn the distribution of trajectories by parameterizing the transition function $f$ explicitly as an element in a function space.

Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)

no code yet • 18 Mar 2024

Based on the work of (Bates et al., 2022), we define new conformal AUROC and conformal FRP@TPR95 metrics, which are corrections that provide probabilistic conservativeness guarantees on the variability of these metrics.

Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion

no code yet • 16 Mar 2024

When trained using KNN on CIFAR10, MLOD-Fisher significantly lowers the false positive rate (FPR) from 24. 09% to 7. 47% on average compared to merely utilizing the features of the last layer.

Energy Correction Model in the Feature Space for Out-of-Distribution Detection

no code yet • 15 Mar 2024

In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier.

Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks

no code yet • 13 Mar 2024

Deep Neural Networks (DNNs) do not inherently compute or exhibit empirically-justified task confidence.

Towards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection

no code yet • 12 Mar 2024

Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been established that would allow such systems to be deployed in safety-critical applications.

COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

no code yet • 11 Mar 2024

The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind.