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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 101–125 of 629 papers

TitleStatusHype
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy PredictionCode1
On Out-of-distribution Detection with Energy-based ModelsCode1
On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imagingCode1
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic FeaturesCode1
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized EmbeddingsCode1
OODformer: Out-Of-Distribution Detection TransformerCode1
Deep Anomaly Detection with Outlier ExposureCode1
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution DataCode1
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution ShiftsCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile RobotCode1
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic AssemblyCode1
Beyond AUROC & co. for evaluating out-of-distribution detection performanceCode1
Out-of-Distribution Detection with a Single Unconditional Diffusion ModelCode1
Hierarchical VAEs Know What They Don't KnowCode1
Heatmap-based Out-of-Distribution DetectionCode1
Block Selection Method for Using Feature Norm in Out-of-distribution DetectionCode1
Out-of-domain Detection for Natural Language Understanding in Dialog SystemsCode1
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Hyperdimensional Feature Fusion for Out-Of-Distribution DetectionCode1
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
Provably Robust Detection of Out-of-distribution Data (almost) for freeCode1
In or Out? Fixing ImageNet Out-of-Distribution Detection EvaluationCode1
Reliability in Semantic Segmentation: Can We Use Synthetic Data?Code1
Learnability and Algorithm for Continual LearningCode1
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