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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 51–75 of 629 papers

TitleStatusHype
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language ModelsCode1
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
Continual Learning Based on OOD Detection and Task MaskingCode1
Heatmap-based Out-of-Distribution DetectionCode1
ID-like Prompt Learning for Few-Shot Out-of-Distribution DetectionCode1
Beyond AUROC & co. for evaluating out-of-distribution detection performanceCode1
Improving GAN Training via Feature Space ShrinkageCode1
Block Selection Method for Using Feature Norm in Out-of-distribution DetectionCode1
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD DetectionCode1
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language ModelsCode1
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic AssemblyCode1
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
A Rate-Distortion View of Uncertainty QuantificationCode1
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language ModelsCode1
Can multi-label classification networks know what they don't know?Code1
Can multi-label classification networks know what they don’t know?Code1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and BenchmarksCode1
Deep Anomaly Detection with Outlier ExposureCode1
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