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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
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family DistributionsCode1
MOOD: Multi-level Out-of-distribution DetectionCode1
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
SSD: A Unified Framework for Self-Supervised Outlier DetectionCode1
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural NetworksCode1
Make Sure You're Unsure: A Framework for Verifying Probabilistic SpecificationsCode1
Hierarchical VAEs Know What They Don't KnowCode1
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay BufferCode1
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
MASKER: Masked Keyword Regularization for Reliable Text ClassificationCode1
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD DetectionCode1
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
Improved Contrastive Divergence Training of Energy Based ModelsCode1
Feature Space Singularity for Out-of-Distribution DetectionCode1
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular DataCode1
Uncertainty Aware Semi-Supervised Learning on Graph DataCode1
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
FOOD: Fast Out-Of-Distribution DetectorCode1
Certifiably Adversarially Robust Detection of Out-of-Distribution DataCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
ATOM: Robustifying Out-of-distribution Detection Using Outlier MiningCode1
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-CountsCode1
Why Normalizing Flows Fail to Detect Out-of-Distribution DataCode1
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown ExamplesCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
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