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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 226–250 of 629 papers

TitleStatusHype
Conservative Prediction via Data-Driven Confidence MinimizationCode0
Enhancing Out-of-Distribution Detection in Medical Imaging with Normalizing FlowsCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Confidence-based Out-of-Distribution Detection: A Comparative Study and AnalysisCode0
Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned Context OptimizationCode0
Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General FrameworkCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
Gradient-Regularized Out-of-Distribution DetectionCode0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
Concept Matching with Agent for Out-of-Distribution DetectionCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
Harnessing Out-Of-Distribution Examples via Augmenting Content and StyleCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Revealing the Distributional Vulnerability of Discriminators by Implicit GeneratorsCode0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
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