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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 191–200 of 629 papers

TitleStatusHype
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic GraphsCode0
Contrastive Learning for OOD in Object detectionCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
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