Out-of-Distribution Detection
326 papers with code • 50 benchmarks • 22 datasets
Detect out-of-distribution or anomalous examples.
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Latest papers with no code
Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model
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
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?)
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
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
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
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
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
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.
Trustworthy Partial Label Learning with Out-of-distribution Detection
PLL-OOD significantly enhances model adaptability and accuracy by merging self-supervised learning with partial label loss and pioneering the Partial-Energy (PE) score for OOD detection.
Advancing Out-of-Distribution Detection through Data Purification and Dynamic Activation Function Design
Our work addresses this challenge by enhancing the detection and management of OOD samples in neural networks.