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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 51–100 of 629 papers

TitleStatusHype
Can multi-label classification networks know what they don’t know?Code1
MOOD: Multi-level Out-of-distribution DetectionCode1
Detection of out-of-distribution samples using binary neuron activation patternsCode1
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution DetectionCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
Beyond AUROC & co. for evaluating out-of-distribution detection performanceCode1
Negative Label Guided OOD Detection with Pretrained Vision-Language ModelsCode1
Block Selection Method for Using Feature Norm in Out-of-distribution DetectionCode1
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object DetectionCode1
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy PredictionCode1
DICE: Leveraging Sparsification for Out-of-Distribution DetectionCode1
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
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
A Rate-Distortion View of Uncertainty QuantificationCode1
EAT: Towards Long-Tailed 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
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
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
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?Code1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
A framework for benchmarking class-out-of-distribution detection and its application to ImageNetCode1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution DetectionCode1
A Theoretical Study on Solving Continual LearningCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
Augmenting Softmax Information for Selective Classification with Out-of-Distribution DataCode1
Hierarchical VAEs Know What They Don't KnowCode1
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile RobotCode1
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language ModelsCode1
Improving GAN Training via Feature Space ShrinkageCode1
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
Continual Learning Based on OOD Detection and Task MaskingCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
A Multi-Head Model for Continual Learning via Out-of-Distribution ReplayCode1
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
Balanced Energy Regularization Loss for Out-of-distribution DetectionCode1
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoderCode1
Deep Anomaly Detection with Outlier ExposureCode1
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized EmbeddingsCode1
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