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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 401–425 of 629 papers

TitleStatusHype
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackCode2
Full-Spectrum Out-of-Distribution Detection—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
RODD: A Self-Supervised Approach for Robust Out-of-Distribution DetectionCode1
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
Continual Learning Based on OOD Detection and Task MaskingCode1
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the WildCode1
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?Code1
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasksCode1
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps—0
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
Training OOD Detectors in their Natural HabitatsCode1
VOS: Learning What You Don't Know by Virtual Outlier SynthesisCode2
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANsCode1
Out of Distribution Detection on ImageNet-OCode0
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
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