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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 126–150 of 629 papers

TitleStatusHype
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Energy-based Out-of-Distribution Detection for Graph Neural NetworksCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized EmbeddingsCode1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
Exploring the Limits of Out-of-Distribution DetectionCode1
Feature Space Singularity for Out-of-Distribution DetectionCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic FeaturesCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
A Rate-Distortion View of Uncertainty QuantificationCode1
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and BenchmarksCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum EntropyCode1
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
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
ID-like Prompt Learning for Few-Shot Out-of-Distribution DetectionCode1
Improved Contrastive Divergence Training of Energy Based ModelsCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
InFlow: Robust outlier detection utilizing Normalizing FlowsCode1
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language ModelsCode1
MASKER: Masked Keyword Regularization for Reliable Text ClassificationCode1
OODD: Test-time Out-of-Distribution Detection with Dynamic DictionaryCode1
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