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Out of Distribution (OOD) Detection

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, as the model has not encountered it during training.

OOD detection is typically performed by training a model to distinguish between in-distribution (ID) data, which the model has seen during training, and OOD data, which it has not seen. This can be done using a variety of techniques, such as training a separate OOD detector, or modifying the model's architecture or loss function to make it more sensitive to OOD data.

Papers

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Showing 501–525 of 629 papers

TitleStatusHype
Sample-dependent Adaptive Temperature Scaling for Improved CalibrationCode0
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution DetectionCode0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
Being a Bit Frequentist Improves Bayesian Neural NetworksCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution DetectionCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Detecting semantic anomaliesCode0
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to RankCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
SeTAR: Out-of-Distribution Detection with Selective Low-Rank ApproximationCode0
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class BoundariesCode0
kFolden: k-Fold Ensemble for Out-Of-Distribution DetectionCode0
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution DetectionCode0
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