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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 251–275 of 629 papers

TitleStatusHype
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD DetectionCode0
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image ModelsCode0
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution DetectionCode0
SR-OOD: Out-of-Distribution Detection via Sample Repairing—0
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to RankCode0
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain DetectionCode0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes—0
A Survey on Out-of-Distribution Detection in NLP—0
Out-of-distribution detection algorithms for robust insect classification—0
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
Multidimensional Uncertainty Quantification for Deep Neural Networks—0
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning—0
Unified Out-Of-Distribution Detection: A Model-Specific Perspective—0
GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution DetectionCode1
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
AUTO: Adaptive Outlier Optimization for Test-Time OOD DetectionCode0
Reliability in Semantic Segmentation: Are We on the Right Track?Code1
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing—0
MCROOD: Multi-Class Radar Out-Of-Distribution Detection—0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Improving GAN Training via Feature Space ShrinkageCode1
Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar—0
VRA: Variational Rectified Activation for Out-of-distribution Detection—0
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